Learning asexual information recommendation method and system based on artificial intelligence

Abstract

The invention provides an overseas study information recommendation method and system based on artificial intelligence, and relates to the technical field of overseas study information data processing. The method comprises the steps of collecting historical overseas study information recommendation data, and performing demand classification clustering analysis based on historical overseas study objects to form overseas study information matching classification data; obtaining target demand information of the target study abroad object, and performing demand decomposition to form corresponding target demand decomposition information; and according to the target demand decomposition information of the target study-overseas object and in combination with study-overseas information matching classification data, performing information recommendation analysis to form target study-overseas recommendation information. According to the method, more efficient, accurate and comprehensive overseas study information is provided for the current overseas study demand by analyzing and processing the historical overseas study recommendation information in an artificial intelligence mode.

Description

Technical Field

[0001] The present invention relates to the technical field of overseas study information data processing, and in particular to an overseas study information recommendation method and system based on artificial intelligence. Background Art

[0002] Study abroad information covers many comprehensive aspects including visa policies, academic certification, internships, living necessities, financial preparation, school applications, background improvement, etc. Therefore, for students and parents who want to study abroad, mastering sufficient study abroad information is a prerequisite for making reasonable study abroad plans.

[0003] Currently, there are a large number of overseas study information service agencies. Although these agencies have realized consultation on the overseas study needs of different students and parents more professionally and efficiently, the recommendation of overseas study information is still basically completed through manual collection of information and processing. In addition, since different service personnel have different views and working methods, there will be different degrees of information bias, which cannot efficiently and reasonably form complete overseas study information.

[0004] Therefore, designing an artificial intelligence-based study abroad information recommendation method and system, which uses artificial intelligence to analyze and process historical study abroad recommendation information to provide more efficient, accurate and comprehensive study abroad information for current study abroad needs, is an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a method for recommending study abroad information based on artificial intelligence. By obtaining the study abroad recommendation information of historical study abroad objects and performing data division based on historical classification categories, the division method formed can realize the reasonable information decomposition of study abroad demand information, and then realize the accurate grasp of the study abroad demand in various necessary demand aspects, so as to provide important data collection guidance for subsequent perfect and accurate study abroad recommendation information. At the same time, the study abroad recommendation information finally determined by the historical study abroad object is associated with the demand division information to ensure that each demand category can have reasonable corresponding study abroad recommendation information, so that when collecting study abroad recommendation information based on study abroad demand, historical big data can be used more efficiently to perform different combinations based on demand categories, forming more accurate and comprehensive study abroad recommendation information for demand. On the one hand, by classifying and dividing the demand information, a complete interpretation of the study abroad demand information and a more direct and accurate collection of corresponding information based on the classification information can be realized. On the other hand, the use of big data information to guide the recommendation information can save more time-consuming data collection, and can also ensure the accuracy and completeness of the formed recommendation information, so that the study abroad recommendation information provides more effective study abroad guidance for the demand object.

[0006] The purpose of the present invention is also to provide an artificial intelligence-based study abroad information recommendation system, which is configured to use the study abroad recommendation information of historical study abroad objects to classify needs and match study abroad information, so as to provide further complete study abroad recommendation information as a whole more efficiently and accurately for new study abroad needs, ensure the efficient and accurate formation of study abroad recommendation information, and is the material basis for realizing the efficient and accurate provision of study abroad recommendation information.

[0007] In a first aspect, the present invention provides a method for recommending study abroad information based on artificial intelligence, comprising collecting historical study abroad information recommendation data, and performing demand classification and clustering analysis based on historical study abroad objects to form study abroad information matching classification data; obtaining target demand information of target study abroad objects, and performing demand decomposition to form corresponding target demand decomposition information; performing information recommendation analysis based on the target demand decomposition information of the target study abroad objects and combining the study abroad information matching classification data to form target study abroad recommendation information.

[0008] In the present invention, the method obtains the study abroad recommendation information of the historical study abroad object and divides the data based on the historical classification category. The formed division method can realize the reasonable information decomposition of the study abroad demand information, and then realize the accurate grasp of the study abroad demand in each necessary demand aspect, so as to provide important data collection guidance for the subsequent perfect and accurate study abroad recommendation information. At the same time, the study abroad recommendation information finally determined by the historical study abroad object is associated with the demand division information to ensure that each demand category can have a reasonable corresponding study abroad recommendation information. In this way, when collecting the study abroad recommendation information based on the study abroad demand, the historical big data can be used more efficiently to perform different combinations based on the demand category, forming more accurate and comprehensive study abroad recommendation information for the demand. On the one hand, by classifying and dividing the demand information, a complete interpretation of the study abroad demand information and a more direct and accurate collection of corresponding information based on the classification information can be realized. On the other hand, the use of big data information to guide the recommendation information can save more time-consuming data collection, and can also ensure the accuracy and completeness of the formed recommendation information, so that the study abroad recommendation information provides more effective study abroad guidance for the demand object.

[0009] As a possible implementation method, historical study abroad information recommendation data is collected, and demand classification and clustering analysis based on historical study abroad objects is performed to form study abroad information matching classification data, including: extracting historical study abroad demand classification information of different historical study abroad objects in the historical study abroad information recommendation data, and forming a historical study abroad demand classification data set; clustering the historical study abroad demand classification data set based on the classification level to form historical study abroad demand classification clustering data; extracting historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data, and matching and clustering the study abroad information in combination with the historical study abroad demand classification clustering data to form study abroad information matching classification data.

[0010] In the present invention, the historical study abroad information recommendation data is used to realize the reasonable study abroad information recommendation for the study abroad demand. The most important thing is to establish the corresponding relationship between the study abroad demand information and the recommended study abroad information through the historical study abroad information recommendation data, so that after obtaining the new study abroad demand information, the accurate recommendation information can be quickly determined based on the study abroad demand. Therefore, the processing of the historical study abroad information recommendation data is firstly a reasonable analysis of the demand information. Here, considering that the study abroad demand information itself will be obtained in different aspects when it is obtained in order to prepare targeted materials more reasonably, the classification of the study abroad demand information itself is an objective fact. This classification is usually also the demand information acquisition method adopted by most study abroad consulting service agencies, such as establishing a table for different demand aspects to enter the demand information of different aspects and then sorting and collecting the materials for the specific needs of the different aspects entered. Therefore, the demand information analysis of the historical study abroad information recommendation data can use this division of different aspects to realize reasonable analysis and processing. On this basis, the study abroad recommendation information finally obtained by the historical study abroad object can be matched according to the different aspects of demand, forming a structured study abroad recommendation data for reference.

[0011] As a possible implementation method, the historical study abroad demand classification data set is clustered based on the classification hierarchy to form historical study abroad demand classification clustering data, including: according to the historical study abroad demand classification information of different historical study abroad objects in the historical study abroad demand classification data set, different categories of study abroad demand information and different classification levels under different categories are determined, and the hierarchy order is determined according to the inclusion relationship of different classification levels under the same category to form a study abroad demand classification hierarchy table; according to the study abroad demand classification hierarchy table, the historical study abroad demand classification information of different historical study abroad objects is classified and matched to form matching data of the historical demand classification hierarchy table; the historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data is extracted, and the matching and clustering of the study abroad information is performed in combination with the matching data of the historical study abroad demand classification hierarchy table to form matching classification data of the study abroad information.

[0012] In the present invention, the classification hierarchical clustering of historical study abroad demand classification data is mainly in three aspects. One aspect is to reasonably divide the demand classification data according to the objective classification method to establish the institutionalized data of the demand category. The second aspect is to reasonably organize the demand information based on this structured classification method. The third aspect is to match the study abroad recommendation information on this basis. Here, it should be noted that the classification hierarchical table formed according to the objective classification method of the demand information itself is very complete in terms of category and hierarchy because it is formed based on big data. At the same time, because historical big data can be continuously updated, the hierarchical table has a certain scalability, can adapt to the ever-changing study abroad needs, and fully meet the accurate classification and division of study abroad demand information, so as to ensure the accuracy and completeness of the subsequent recommendation information formed for the study abroad needs.

[0013] As a possible implementation method, the historical study abroad demand classification information of different historical study abroad objects is classified and matched according to the study abroad demand classification hierarchy table to form historical demand classification hierarchy table matching data, including: for different historical study abroad objects, the corresponding historical study abroad demand classification information is divided based on information categories to form historical object category division information; for the category division information of different information categories under different historical study abroad objects, the information hierarchy is divided according to the different classification levels corresponding to different categories in the historical demand classification hierarchy table to form historical object category hierarchy division information under the information category corresponding to the historical study abroad object; for the historical object category hierarchy division information of the same information category of different historical study abroad objects, the hierarchy order in the historical demand classification hierarchy table is clustered to form historical demand category hierarchy matching data corresponding to different information categories; the historical demand category hierarchy matching data of all information categories are aggregated, and the data is structured according to the historical demand classification hierarchy table to form historical demand classification hierarchy table matching data.

[0014] In the present invention, the classification of overseas study needs is mainly based on an objective historical classification method. This classification includes two dimensions of classification. One dimension is to divide different information categories to form completely different information categories, such as regions, capital composition, etc. The second dimension is the division of different levels under different information categories, such as different levels formed in the order of inclusion relationships under the region from high to low can be continents, countries, provinces or states, etc. Since the division method of demand information is objective, when the demand information is split to match the formed classification hierarchy table, only simple word segmentation or sentence segmentation extraction is required. If necessary, reasonable semantic extraction can be performed on lengthy sentences. And because the demand information under big data is clustered, the corresponding information amount under different information categories can be sufficient, and then different levels under the information category form parallel and unrelated information to ensure that the subsequent positioning and matching of demand information can be achieved efficiently and quickly.

[0015] As a possible implementation method, the historical object category hierarchical division information of the same information category of different historical study-abroad objects is clustered based on the hierarchical order in the historical demand classification hierarchical table to form historical demand category hierarchical matching data corresponding to different information categories, including: extracting different hierarchical division information of different historical study-abroad objects in turn according to the hierarchical order from high to low in the historical demand classification hierarchical table, and performing the following clustering processing: if the same hierarchical division information corresponding to any two historical study-abroad objects is the same, then the hierarchical division information of the corresponding level is merged as the comparison result of the corresponding level, and the hierarchical division information on the subsequent level is continued to be compared and analyzed; if the same hierarchical division information corresponding to any two historical study-abroad objects has an inclusion relationship, then the hierarchical division information with a larger inclusion range is used as the comparison result of the two historical study-abroad objects at the corresponding level, and the hierarchical division information on the subsequent level is continued to be compared and analyzed. Comparison analysis; if the same level division information corresponding to any two historical study-abroad objects is different, the level division information corresponding to the two historical study-abroad objects is retained as the comparison result of the corresponding level, and the level division information of the two historical study-abroad objects is no longer compared on the subsequent level; if the same level division information corresponding to any two historical study-abroad objects has an intersection relationship, and the historical matching study-abroad information determined by the two historical study-abroad objects is different, then the intersection of the two level division information is removed and both are used as the comparison result on the corresponding level, and the level division information of the two historical study-abroad objects is no longer compared on the subsequent level; if the same level division information corresponding to any two historical study-abroad objects has an intersection, and the historical matching study-abroad information determined by the two historical study-abroad objects is the same, then the result after the AND operation of the two level division information is used as the comparison result on the corresponding level, and the level division information on the subsequent level is continued to be compared and analyzed.

[0016] In the present invention, here, there is a certain clustering order for the hierarchical matching of historical demand information, and this clustering order is from high to low. For example, for regions, only when there is the same in the previous level will the next level be compared. For example, if the demand in the previous level is all Europe, then the comparison of whether the European countries are the same will continue in the next level. If they are the same, they will be merged into one. If they are not the same, they are two different countries selected at this level, and then the hierarchical comparison will be performed again. Of course, the information on the previous level is already different, so there is no need to compare the next level. For considering hierarchical information clustering such as finance, since this type of information is usually range information, such as how much tuition is per year, it may appear that different classified information that is actually parallel at the same level does have overlapping ranges. In order to better distinguish, it is necessary to exclude the overlapping range. Therefore, when using this hierarchical data in the future, if the range of the demand is not on the level, it can be determined by comparing the proximity or using a lower regional range as a corresponding, or providing options to the demand object. Through such clustering of historical demand information, it is possible to clearly divide the types and levels of demand, laying a data foundation for subsequent accurate information positioning.

[0017] As a possible implementation method, historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data is extracted, and the study abroad information is matched and clustered in combination with the matching data of the historical study abroad demand classification hierarchy table to form the study abroad information matching classification data, including: according to the historical matching study abroad information of different historical study abroad objects, the matching schools of different historical study abroad objects are determined; according to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study abroad objects are matched and clustered to form the study abroad information matching classification data.

[0018] In the present invention, after the reasonable classification and division of the demand information, in order to quickly locate the corresponding study abroad information based on the demand information, it is necessary to associate the historical study abroad information with the divided demand information. It should be noted here that for associating the historical study abroad recommendation information with the demand information, since the recommendation information is also aimed at various aspects of different needs, in order to facilitate the subsequent output and accurate positioning of the recommendation information and avoid the matching and positioning of multiple aspects or dimensions, the school that was last matched with the historical study abroad is used as the associated information to be associated with the demand classification data. In this way, after the school is located for the target demand information in the future, the study abroad information corresponding to the historical study abroad of the associated school under the classification data can be merged and completely output. On the one hand, it will not cause confusion in demand matching, and on the other hand, it also ensures the accuracy and completeness of the study abroad recommendation information, and also makes the efficiency of the study abroad information recommendation more efficient.

[0019] As a possible implementation method, according to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study abroad objects are matched and clustered to form the matching classification data of study abroad information, including: according to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study abroad objects are matched and clustered in the following manner: for different information categories in the matching data of the historical demand classification hierarchy table, if the hierarchical division information of all levels under the corresponding information category for different historical study abroad objects is the same, and the matching schools are the same, then the matching schools are associated with the hierarchical division information on the level with the lowest hierarchical order; for different information categories in the matching data of the historical demand classification hierarchy table, if the hierarchical division information of all levels under the corresponding information category for different historical study abroad objects is the same, but the matching schools are different, then the different The matching schools are associated with the hierarchical division information on the lowest level in the hierarchical order; for different information categories in the historical demand classification hierarchical table matching data, if different historical study-abroad objects have different hierarchical division information on all levels under the corresponding information categories, but the matching schools are the same, then the matching schools are respectively associated with the hierarchical division information on the lowest level in the hierarchical order corresponding to the different hierarchical division information; for different information categories in the historical demand classification hierarchical table matching data, if different historical study-abroad objects have different hierarchical division information on all levels under the corresponding information categories, and the matching schools are also different, then according to the historical study-abroad demand classification information of the historical study-abroad objects corresponding to the matching schools, different matching schools are associated with the hierarchical division information on the lowest level in the hierarchical order under the corresponding hierarchical division information.

[0020] In the present invention, the study abroad recommendation information is associated with the demand classification information. On the basis of using schools as the associated objects, the association is performed for different information categories, that is, the demand information formed by all hierarchical connections in any information category is an information category of the study abroad demand of the historical study abroad object. Then, the school matching the historical study abroad object can be associated with the demand information formed by the hierarchical connection corresponding to this information category. Of course, the lowest level association can achieve the uniqueness of the association. In the association process, it may be encountered that the demand information formed by different hierarchical connections may have different matching schools corresponding to them, so these different matching schools can be directly associated with it.

[0021] As a possible implementation method, the target demand information of the target study abroad object is obtained, and the demand is decomposed to form corresponding target demand decomposition information, including: splitting the target demand information according to the information categories in the historical demand classification hierarchy table to form target demand category division information; hierarchically dividing the information of different information categories in the target demand classification information according to the classification hierarchy of different information categories in the historical demand classification hierarchy table to form target category hierarchy division data; and gathering the target category hierarchy division data corresponding to all information categories to form target demand decomposition information.

[0022] In the present invention, in order to recommend study abroad information to target study abroad objects, the target study abroad needs must first be decomposed, and the decomposition is performed based on the category hierarchy table formed by big data to match the historical big data to ensure that the historical data can be quickly and efficiently referenced when the recommendation information is subsequently formed. The decomposition includes two dimensions, namely the division of information categories and the hierarchical division under the information categories.

[0023] As a possible implementation method, the target needs of the target study abroad objects are decomposed into information, and information recommendation analysis is performed in combination with the study abroad information matching classification data to form target study abroad recommendation information, including: dividing the data into different target categories in the target needs decomposition information, and determining the category matching target schools corresponding to different information categories according to the study abroad information matching classification data; performing the following matching recommendation analysis according to the different category matching target schools: if the different category matching target schools are the same, the category matching target school is determined as the target matching school, and the complete recommendation information corresponding to the target matching school is output; if the different category matching target schools are different, the different category matching target schools are repeatedly counted, and the complete recommendation information of the corresponding category matching target school is output in descending order according to the count size.

[0024] In the present invention, after the target needs are divided, the overseas study information matching classification data can be used to locate the matching schools according to different information categories. Of course, there may be multiple corresponding matching schools when locating. In order to ensure a better matching degree, the matching schools are arranged in order according to the number of matching occurrences in different information categories. The matching school with the highest repetition degree satisfies the most demand information categories and is also the most suitable for the target object.

[0025] In a second aspect, the present invention provides an artificial intelligence-based study abroad information recommendation system, which is configured to: collect historical study abroad information recommendation data, and perform demand classification and clustering analysis based on historical study abroad objects to form study abroad information matching classification data; obtain target demand information of target study abroad objects, and perform demand decomposition to form corresponding target demand decomposition information; perform information recommendation analysis based on the target demand decomposition information of the target study abroad objects, and combine the study abroad information matching classification data to form target study abroad recommendation information.

[0026] In the present invention, the system is configured to utilize the study abroad recommendation information of historical study abroad objects to classify needs and match study abroad information, so as to provide further complete study abroad recommendation information as a whole more efficiently and accurately for new study abroad needs, thereby ensuring the efficient and accurate formation of study abroad recommendation information, and is the material basis for realizing the efficient and accurate provision of study abroad recommendation information.

[0027] The beneficial effects of the study abroad information recommendation method and system based on artificial intelligence provided by the present invention are as follows:

[0028] The method obtains the study abroad recommendation information of the historical study abroad object and divides the data based on the historical classification category. The formed division method can realize the reasonable information decomposition of the study abroad demand information, and then realize the accurate grasp of the study abroad demand in various necessary demand aspects, so as to provide important data collection guidance for the subsequent provision of complete and accurate study abroad recommendation information. At the same time, the study abroad recommendation information finally determined by the historical study abroad object is associated with the demand division information to ensure that each demand category can have reasonable corresponding study abroad recommendation information. In this way, when collecting the study abroad recommendation information based on the study abroad demand, the historical big data can be used more efficiently to make different combinations based on the demand categories, so as to form more accurate and comprehensive study abroad recommendation information for the demand. On the one hand, by classifying the demand information, the complete interpretation of the study abroad demand information and the more direct and accurate corresponding information collection based on the classification information can be realized. On the other hand, the use of big data information to guide the recommendation information can save more time-consuming data collection, and can also ensure the accuracy and completeness of the recommendation information formed, so that the study abroad recommendation information provides more effective study abroad guidance for the demand object.

[0029] The system is configured to utilize the study abroad recommendation information of historical study abroad objects to classify needs and match study abroad information, and to provide further complete study abroad recommendation information as a whole for new study abroad needs in a more efficient and accurate manner, thereby ensuring the efficient and accurate formation of study abroad recommendation information, and is the material basis for achieving efficient and accurate provision of study abroad recommendation information. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A step diagram of a method for recommending study abroad information based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] Study abroad information covers many comprehensive aspects including visa policies, academic certification, internships, living necessities, financial preparation, school applications, background improvement, etc. Therefore, for students and parents who want to study abroad, mastering sufficient study abroad information is a prerequisite for making reasonable study abroad plans.

[0034] Currently, there are a large number of overseas study information service agencies. Although these agencies have realized consultation on the overseas study needs of different students and parents more professionally and efficiently, the recommendation of overseas study information is still basically completed through manual collection of information and processing. In addition, since different service personnel have different views and working methods, there will be different degrees of information bias, which cannot efficiently and reasonably form complete overseas study information.

[0035] refer to Figure 1The embodiment of the present invention provides a method for recommending study abroad information based on artificial intelligence. The method obtains the study abroad recommendation information of historical study abroad objects and divides the data based on historical classification categories. The formed division method can realize the reasonable information decomposition of the study abroad demand information, and then realize the accurate grasp of the study abroad demand in various necessary demand aspects, so as to provide important data collection guidance for the subsequent provision of complete and accurate study abroad recommendation information. At the same time, the study abroad recommendation information finally determined by the historical study abroad object is associated with the demand division information to ensure that each demand category can have reasonable corresponding study abroad recommendation information. In this way, when collecting the study abroad recommendation information based on the study abroad demand, the historical big data can be used more efficiently to perform different combinations based on the demand categories to form more accurate and comprehensive study abroad recommendation information for the demand. On the one hand, by classifying the demand information, the complete interpretation of the study abroad demand information and the more direct and accurate corresponding information collection based on the classification information can be realized. On the other hand, the use of big data information to guide the recommendation information can save more time-consuming data collection, and can also ensure the accuracy and completeness of the formed recommendation information, so that the study abroad recommendation information provides more effective study abroad guidance for the demand object.

[0036] The method for recommending study abroad information based on artificial intelligence specifically includes the following steps:

[0037] S1: Collect historical study abroad information recommendation data, and conduct demand classification and clustering analysis based on historical study abroad targets to form study abroad information matching classification data.

[0038] Collect historical study abroad information recommendation data, and conduct demand classification and clustering analysis based on historical study abroad objects to form study abroad information matching classification data, including: extracting historical study abroad demand classification information of different historical study abroad objects in the historical study abroad information recommendation data, and forming a historical study abroad demand classification data set; clustering the historical study abroad demand classification data set based on the classification level to form historical study abroad demand classification clustering data; extracting historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data, and matching and clustering the study abroad information in combination with the historical study abroad demand classification clustering data to form study abroad information matching classification data.

[0039] The most important thing to achieve reasonable recommendation of study abroad information for study abroad needs by using historical study abroad information recommendation data is to establish a corresponding relationship between study abroad demand information and recommended study abroad information through historical study abroad information recommendation data, so that after obtaining new study abroad demand information, accurate recommendation information can be quickly determined based on study abroad demand. Therefore, the processing of historical study abroad information recommendation data is first of all a reasonable analysis of demand information. Here, considering that the study abroad demand information itself will be obtained in different aspects when it is obtained in order to prepare targeted materials more reasonably, the classification of study abroad demand information itself is an objective fact. This classification is usually also the way to obtain demand information adopted by most study abroad consulting service agencies, such as establishing tables for different demand aspects to enter different demand information and then sorting and collecting materials for the specific needs of different aspects entered. Therefore, the demand information analysis of historical study abroad information recommendation data can use this division of different aspects to achieve reasonable analysis and processing. On this basis, the study abroad recommendation information finally obtained by the historical study abroad object can be matched according to different aspects of demand to form structured study abroad recommendation data for reference.

[0040] The historical study abroad demand classification data set is clustered based on the classification hierarchy to form historical study abroad demand classification clustering data, including: according to the historical study abroad demand classification information of different historical study abroad objects in the historical study abroad demand classification data set, different categories of study abroad demand information and different classification levels under different categories are determined, and the hierarchy order is determined according to the inclusion relationship of different classification levels under the same category to form a study abroad demand classification hierarchy table; according to the study abroad demand classification hierarchy table, the historical study abroad demand classification information of different historical study abroad objects is classified and matched to form matching data of the historical demand classification hierarchy table; the historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data is extracted, and the matching and clustering of the study abroad information is performed in combination with the matching data of the historical study abroad demand classification hierarchy table to form matching classification data of the study abroad information.

[0041] The classification hierarchical clustering of historical study abroad demand classification data mainly includes three aspects: one is to reasonably divide the demand classification data according to the objective classification method and establish the institutionalized data of demand categories; the second is to reasonably organize the demand information based on this structured classification method; the third is to match the study abroad recommendation information on this basis. Here, it should be noted that the classification hierarchical table formed according to the objective classification method of the demand information itself is very complete in terms of categories and hierarchical integrity because it is formed based on big data. At the same time, because historical big data can be continuously updated, the hierarchical table has a certain degree of scalability and can adapt to the ever-changing study abroad needs, fully meet the accurate classification and division of study abroad demand information, so as to ensure the accuracy and completeness of the subsequent recommendation information formed for study abroad needs.

[0042] According to the classification hierarchy table of study abroad needs, the historical study abroad needs classification information of different historical study abroad objects is classified and matched to form matching data of the historical needs classification hierarchy table, including: for different historical study abroad objects, the corresponding historical study abroad needs classification information is divided based on information categories to form historical object category division information; for the category division information of different information categories under different historical study abroad objects, the information hierarchy is divided according to the different classification hierarchies corresponding to different categories in the historical needs classification hierarchy table to form historical object category hierarchy division information under the information category corresponding to the historical study abroad object; for the historical object category hierarchy division information of the same information category of different historical study abroad objects, the hierarchy order in the historical needs classification hierarchy table is clustered to form historical needs category hierarchy matching data corresponding to different information categories; the historical needs category hierarchy matching data of all information categories are aggregated, and the data is structured according to the historical needs classification hierarchy table to form historical needs classification hierarchy table matching data.

[0043] The classification of study abroad needs is mainly based on an objective historical classification method. This classification includes two dimensions of classification. One dimension is the division of different information categories to form completely different information categories, such as region, capital composition, etc. The second dimension is the division of different levels under different information categories, such as the different levels formed in the order of inclusion relationship under the region from high to low can be continents, countries, provinces or states, etc. Since the division method of demand information is objective, when splitting the demand information to match the formed classification hierarchy table, it is only necessary to perform simple word segmentation or sentence segmentation extraction. If necessary, reasonable semantic extraction can be performed on lengthy sentences. And because the demand information is clustered based on big data, the corresponding information volume under different information categories can be sufficient, and then the different levels under the information category form parallel and unrelated information, so as to ensure that the positioning and matching of demand information can be achieved efficiently and quickly in the future.

[0044] For the historical object category hierarchical division information of the same information category of different historical study-abroad objects, clustering is performed based on the hierarchical order in the historical demand classification hierarchical table to form historical demand category hierarchical matching data corresponding to different information categories, including: extracting different hierarchical division information of different historical study-abroad objects in turn according to the hierarchical order from high to low in the historical demand classification hierarchical table, and performing the following clustering processing: if the same hierarchical division information corresponding to any two historical study-abroad objects is the same, then the hierarchical division information of the corresponding level is merged as the comparison result of the corresponding level, and the hierarchical division information on the subsequent level is continued to be compared and analyzed; if the same hierarchical division information corresponding to any two historical study-abroad objects has an inclusion relationship, then the hierarchical division information with a larger inclusion range is used as the comparison result of the two historical study-abroad objects at the corresponding level, and the hierarchical division information on the subsequent level is continued to be compared and analyzed; if any If the same level division information corresponding to any two historical study-abroad objects is different, the level division information corresponding to the two historical study-abroad objects is retained as the comparison result at the corresponding level, and the level division information of the two historical study-abroad objects is no longer compared at the subsequent level; if the same level division information corresponding to any two historical study-abroad objects is intersecting, and the historical matching study-abroad information determined by the two historical study-abroad objects is different, then the intersecting parts of the two level division information are removed and both are used as the comparison result at the corresponding level, and the level division information of the two historical study-abroad objects is no longer compared at the subsequent level; if the same level division information corresponding to any two historical study-abroad objects is intersecting, and the historical matching study-abroad information determined by the two historical study-abroad objects is the same, then the result after the AND operation of the two level division information is used as the comparison result at the corresponding level, and the level division information at the subsequent level is continued to be compared and analyzed.

[0045] Here, there is a certain clustering order for the hierarchical matching of historical demand information. This clustering order is from high to low. For example, for regions, only when there is the same in the previous level will the next level be compared. For example, if the demand in the previous level is all Europe, then the comparison of whether the European countries are the same will continue in the next level. If they are the same, they will be merged into one. If they are not the same, they are two different countries selected at this level, and then the hierarchical comparison will be carried out again. Of course, the information on the previous level is already different, so there is no need to compare the next level. For considering hierarchical information clustering such as finance, since this type of information is usually range information, such as how much tuition is per year, it may appear that different classified information that is actually parallel at the same level does have overlapping ranges. In order to better distinguish, it is necessary to exclude the overlapping range. Therefore, when using this hierarchical data in the future, if the range of the demand is not at the level, it can be determined by comparing the proximity or using a lower regional range as a corresponding, or providing options to the demand object. Through such clustering of historical demand information, it is possible to clearly divide the types and levels of demand, laying a data foundation for subsequent accurate information positioning.

[0046] The historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data is extracted, and the study abroad information is matched and clustered in combination with the matching data of the historical study abroad demand classification hierarchy table to form the study abroad information matching classification data, including: according to the historical matching study abroad information of different historical study abroad objects, the matching schools of different historical study abroad objects are determined; according to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study abroad objects are matched and clustered to form the study abroad information matching classification data.

[0047] After the demand information is reasonably classified and divided, in order to quickly locate the corresponding study abroad information based on the demand information, it is necessary to associate the historical study abroad information with the divided demand information. It should be noted here that for associating the historical study abroad recommendation information with the demand information, since the recommendation information is also aimed at various aspects of different needs, in order to facilitate the subsequent output and accurate positioning of the recommendation information and avoid matching positioning in multiple aspects or dimensions, the school that was last matched for the historical study abroad is used as the associated information to be associated with the demand classification data. In this way, after the target demand information is located at the school, the study abroad information corresponding to the historical study abroad of the associated school under the classification data can be merged and fully output. On the one hand, it will not cause confusion in demand matching, and on the other hand, it also ensures the accuracy and completeness of the study abroad recommendation information, and also makes the efficiency of the study abroad information recommendation more efficient.

[0048] According to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study-abroad objects are matched and clustered to form the matching classification data of study-abroad information, including: according to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study-abroad objects are matched and clustered in the following manner: for different information categories in the matching data of the historical demand classification hierarchy table, if the hierarchical division information of all levels under the corresponding information category for different historical study-abroad objects is the same, and the matching schools are the same, then the matching schools are associated with the hierarchical division information on the lowest level in the hierarchy order; for different information categories in the matching data of the historical demand classification hierarchy table, if the hierarchical division information of all levels under the corresponding information category for different historical study-abroad objects is the same, but the matching schools are different, then the different matching schools are associated with The hierarchical division information on the lowest level in the hierarchical order is associated; for different information categories in the historical demand classification hierarchical table matching data, if different historical study-abroad objects have different hierarchical division information on all levels under the corresponding information categories, but the matching schools are the same, then the matching schools will be respectively associated with the hierarchical division information on the lowest level in the hierarchical order corresponding to the different hierarchical division information; for different information categories in the historical demand classification hierarchical table matching data, if different historical study-abroad objects have different hierarchical division information on all levels under the corresponding information categories, and the matching schools are also different, then according to the historical study-abroad demand classification information of the historical study-abroad objects corresponding to the matching schools, different matching schools will be associated with the hierarchical division information on the lowest level in the hierarchical order under the corresponding hierarchical division information.

[0049] The study abroad recommendation information is associated with the demand classification information. On the basis of using schools as the associated objects, the association is performed for different information categories. That is, the demand information formed by all hierarchical connections in any information category is an information category of the study abroad needs of historical study abroad objects. Then, the schools matching the historical study abroad objects can be associated with the demand information formed by the hierarchical connections corresponding to this information category. Of course, the lowest level association can achieve the uniqueness of the association. In the association process, different demand information formed by different hierarchical connections may have different matching schools, so these different matching schools can be directly associated with it.

[0050] S2: Obtain the target demand information of the target study abroad object, and decompose the demand to form corresponding target demand decomposition information.

[0051] The target demand information of the target study abroad object is obtained, and the demand is decomposed to form corresponding target demand decomposition information, including: splitting the target demand information according to the information categories in the historical demand classification hierarchy table to form target demand category division information; hierarchically dividing the information of different information categories in the target demand classification information according to the classification hierarchy of different information categories in the historical demand classification hierarchy table to form target category hierarchical division data; and gathering the target category hierarchical division data corresponding to all information categories to form target demand decomposition information.

[0052] To recommend study abroad information to target students, we must first decompose the target study abroad needs. The decomposition is based on the category hierarchy table formed by big data to match historical big data to ensure that historical data can be quickly and efficiently referenced when forming recommendation information later. Decomposition includes two dimensions, namely the division of information categories and the hierarchical division under information categories.

[0053] S3: Decompose information according to the target needs of the target study abroad objects, and conduct information recommendation analysis based on the classified data of the study abroad information matching to form the target study abroad recommendation information.

[0054] According to the target demand decomposition information of the target study abroad object, and combined with the study abroad information matching classification data, information recommendation analysis is performed to form the target study abroad recommendation information, including: dividing the data of different target categories in the target demand decomposition information into different levels, and determining the category matching target schools corresponding to different information categories according to the study abroad information matching classification data; according to the different category matching target schools, the following matching recommendation analysis is performed: if the different category matching target schools are the same, the category matching target school is determined as the target matching school, and the complete recommendation information corresponding to the target matching school is output; if the different category matching target schools are different, the different category matching target schools are repeatedly counted, and the complete recommendation information of the corresponding category matching target school is output in descending order according to the count size.

[0055] After dividing the target needs, you can use the overseas study information matching classification data to locate the matching schools according to different information categories. Of course, there may be multiple corresponding matching schools when locating. In order to ensure a better match, the matching schools are arranged in order according to the number of times they appear in different information categories. The school with the highest repetition rate has the most demand information categories and is the most suitable for the target object.

[0056] The present invention also provides an artificial intelligence-based study abroad information recommendation system, which is configured to: collect historical study abroad information recommendation data, and conduct demand classification and clustering analysis based on historical study abroad objects to form study abroad information matching classification data; obtain target demand information of target study abroad objects, and perform demand decomposition to form corresponding target demand decomposition information; perform information recommendation analysis based on the target demand decomposition information of the target study abroad objects and in combination with the study abroad information matching classification data to form target study abroad recommendation information.

[0057] The system is configured to utilize the study abroad recommendation information of historical study abroad objects to classify needs and match study abroad information, and to provide further complete study abroad recommendation information as a whole for new study abroad needs in a more efficient and accurate manner, thereby ensuring the efficient and accurate formation of study abroad recommendation information, and is the material basis for achieving efficient and accurate provision of study abroad recommendation information.

[0058] In summary, the beneficial effects of the method and system for recommending overseas study information based on artificial intelligence provided by the embodiments of the present invention are as follows:

[0059] The method obtains the study abroad recommendation information of the historical study abroad object and divides the data based on the historical classification category. The formed division method can realize the reasonable information decomposition of the study abroad demand information, and then realize the accurate grasp of the study abroad demand in various necessary demand aspects, so as to provide important data collection guidance for the subsequent provision of complete and accurate study abroad recommendation information. At the same time, the study abroad recommendation information finally determined by the historical study abroad object is associated with the demand division information to ensure that each demand category can have reasonable corresponding study abroad recommendation information. In this way, when collecting the study abroad recommendation information based on the study abroad demand, the historical big data can be used more efficiently to make different combinations based on the demand categories, so as to form more accurate and comprehensive study abroad recommendation information for the demand. On the one hand, by classifying the demand information, the complete interpretation of the study abroad demand information and the more direct and accurate corresponding information collection based on the classification information can be realized. On the other hand, the use of big data information to guide the recommendation information can save more time-consuming data collection, and can also ensure the accuracy and completeness of the recommendation information formed, so that the study abroad recommendation information provides more effective study abroad guidance for the demand object.

[0060] The system is configured to utilize the study abroad recommendation information of historical study abroad objects to classify needs and match study abroad information, and to provide further complete study abroad recommendation information as a whole for new study abroad needs in a more efficient and accurate manner, thereby ensuring the efficient and accurate formation of study abroad recommendation information, and is the material basis for achieving efficient and accurate provision of study abroad recommendation information.

[0061] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0062] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0063] It should be understood that the information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0064] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0065] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0066] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0067] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or its similar expression refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0068] It should be understood that the processor in the embodiment of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0069] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0070] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0071] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0072] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0073] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0074] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0080] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for recommending study abroad information based on artificial intelligence, characterized in that: include: Collect historical study abroad information recommendation data, and conduct demand classification and cluster analysis based on historical study abroad targets to form study abroad information matching classification data; Obtain the target demand information of the target study abroad object, and decompose the demand to form corresponding target demand decomposition information; According to the target demand decomposition information of the target study abroad object, and combined with the study abroad information matching classification data, information recommendation analysis is performed to form target study abroad recommendation information.

2. The method for recommending overseas study information based on artificial intelligence according to claim 1, characterized in that: The collecting of historical study abroad information recommendation data and the performing of demand classification and clustering analysis based on historical study abroad objects to form study abroad information matching classification data include: Extracting historical study abroad demand classification information of different historical study abroad objects from the historical study abroad information recommendation data, and forming a historical study abroad demand classification data set; Clustering the historical overseas study demand classification data set based on the classification level to form historical overseas study demand classification clustering data; The historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data is extracted, and the study abroad information is matched and clustered in combination with the historical study abroad demand classification clustering data to form study abroad information matching classification data.

3. The method for recommending overseas study information based on artificial intelligence according to claim 2, characterized in that: The clustering of the historical overseas study demand classification data set based on the classification level to form historical overseas study demand classification clustering data includes: According to the historical study abroad demand classification information of different historical study abroad objects in the historical study abroad demand classification data set, different categories of study abroad demand information and different classification levels under different categories are determined, and the hierarchy order is determined according to the inclusion relationship of different classification levels under the same category to form a study abroad demand classification hierarchy table; According to the study abroad demand classification level table, the historical study abroad demand classification information of different historical study abroad objects is classified and matched to form historical demand classification level table matching data; The historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data is extracted, and the study abroad information is matched and clustered in combination with the matching data of the historical study abroad demand classification hierarchy table to form study abroad information matching classification data.

4. The method for recommending overseas study information based on artificial intelligence according to claim 3, characterized in that: According to the study abroad demand classification level table, the historical study abroad demand classification information of different historical study abroad objects is classified and matched to form historical demand classification level table matching data, including: For different historical study abroad objects, the corresponding historical study abroad demand classification information is divided based on information categories to form historical object category division information; For the classification information of different information categories under different historical study abroad objects, the information hierarchy is divided according to different classification hierarchies corresponding to different categories in the historical demand classification hierarchy table, so as to form the historical object category hierarchy division information under the information category corresponding to the historical study abroad object; Clustering the historical object category hierarchical division information of the same information category of different historical study abroad objects based on the hierarchical order in the historical demand classification hierarchical table to form historical demand category hierarchical matching data corresponding to different information categories; The historical demand category hierarchical matching data of all information categories are collected, and data is structured according to the historical demand classification hierarchy table to form the historical demand classification hierarchy table matching data.

5. The method for recommending overseas study information based on artificial intelligence according to claim 4, characterized in that: The historical object category hierarchical division information of the same information category of different historical study abroad objects is clustered based on the hierarchical order in the historical demand classification hierarchy table to form historical demand category hierarchy matching data corresponding to different information categories, including: According to the hierarchical order from high to low in the historical demand classification hierarchy table, the different hierarchical classification information of different historical study abroad objects is extracted in turn, and the following clustering processing is performed: If the same level division information corresponding to any two of the historical overseas study objects is the same, the level division information of the corresponding level is merged as the comparison result of the corresponding level, and the level division information on the subsequent level is continued to be compared and analyzed; If the same level division information corresponding to any two of the historical study abroad objects has an inclusion relationship, the level division information with a larger inclusion range is used as the comparison result of the two historical study abroad objects at the corresponding level, and the level division information at the subsequent level is continued to be compared and analyzed; If the same level division information corresponding to any two of the historical study abroad objects is different, the level division information corresponding to the two historical study abroad objects is retained as the comparison result of the corresponding level, and the level division information of the two historical study abroad objects is no longer compared at the subsequent level; If the same level division information corresponding to any two of the historical study abroad objects has an intersection relationship, and the historical matching study abroad information determined by the two historical study abroad objects is different, then the intersection of the two level division information is removed and both are used as the comparison result at the corresponding level, and the level division information of the two historical study abroad objects is no longer compared at the subsequent level; If any two of the historical study abroad objects have intersections in the corresponding same level division information, and the historical matching study abroad information determined by the two historical study abroad objects is the same, then the result after the two level division information are combined and calculated is used as the comparison result on the corresponding level, and the level division information on the subsequent level is continued to be compared and analyzed.

6. The method for recommending overseas study information based on artificial intelligence according to claim 5, characterized in that: The extracting the historical matching study abroad information of different historical study abroad objects in the historical study abroad information recommendation data, and performing matching clustering of the study abroad information in combination with the matching data of the historical study abroad demand classification hierarchy table to form the study abroad information matching classification data, includes: Determining matching schools for different historical study abroad objects according to the historical matching study abroad information of different historical study abroad objects; According to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to the different historical study-abroad objects are matched and clustered to form the study-abroad information matching classification data.

7. The method for recommending overseas study information based on artificial intelligence according to claim 6, characterized in that: According to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study abroad objects are matched and clustered to form the study abroad information matching classification data, including: According to the matching data of the historical demand classification hierarchy table, the matching schools corresponding to different historical study abroad objects are matched and clustered in the following manner: For different information categories in the matching data of the historical demand classification hierarchy table, if the hierarchical division information of all levels under the corresponding information categories of different historical study abroad objects is the same, and the matching schools are the same, then the matching school is associated with the hierarchical division information on the lowest level of the hierarchy order; For different information categories in the matching data of the historical demand classification hierarchy table, if the hierarchical division information of all levels under the corresponding information categories of different historical study abroad objects is the same, but the matching schools are different, then the different matching schools are associated with the hierarchical division information on the lowest level of the hierarchy order; For different information categories in the matching data of the historical demand classification hierarchy table, if different historical study abroad objects have different hierarchical division information of all hierarchical levels under the corresponding information categories, but the matching schools are the same, then the matching schools are respectively associated with the hierarchical division information on the lowest hierarchical order corresponding to the different hierarchical division information; For different information categories in the matching data of the historical demand classification hierarchy table, if different historical study-abroad objects have different hierarchical division information of all levels under the corresponding information categories, and the matching schools are also different, then according to the historical study-abroad demand classification information of the historical study-abroad objects corresponding to the matching schools, the different matching schools are associated with the hierarchical division information on the level with the lowest hierarchical order under the corresponding hierarchical division information.

8. The method for recommending overseas study information based on artificial intelligence according to claim 7, characterized in that: The step of obtaining target demand information of target overseas students and performing demand decomposition to form corresponding target demand decomposition information includes: Splitting the target demand information according to the information categories in the historical demand classification hierarchy table to form target demand category division information; According to the classification levels of different information categories in the historical demand classification level table, the information of different information categories in the target demand classification information is hierarchically divided to form target category hierarchical division data; The target category hierarchical division data corresponding to all information categories are collected to form the target demand decomposition information.

9. The method for recommending overseas study information based on artificial intelligence according to claim 8, characterized in that: The target demand decomposition information of the target study abroad object is combined with the study abroad information matching classification data to perform information recommendation analysis to form target study abroad recommendation information, including: For the target category hierarchical classification data in the target demand decomposition information, according to the overseas study information matching classification data, determine the category matching target schools corresponding to the different information categories; According to the above categories, the target schools are matched and the following matching recommendation analysis is performed: If the target schools for different category matching are the same, the target schools for the category matching are determined as the target matching schools, and the complete recommendation information corresponding to the target matching schools is output; If there are different matching target schools for different categories, the different matching target schools for the category are repeatedly counted, and the complete recommendation information of the corresponding matching target schools for the category is output in descending order according to the count size.

10. An artificial intelligence-based study abroad information recommendation system, characterized in that: Configured as: Collect historical study abroad information recommendation data, and conduct demand classification and cluster analysis based on historical study abroad targets to form study abroad information matching classification data; Obtain the target demand information of the target study abroad object, and decompose the demand to form corresponding target demand decomposition information; According to the target demand decomposition information of the target study abroad object, and combined with the study abroad information matching classification data, information recommendation analysis is performed to form target study abroad recommendation information.

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