Digital Display Method of Cultural Resources Based on NLP Technology

Abstract

The present invention discloses a method for digital display of cultural resources based on NLP technology, which relates to the field of language and cultural information technology. The method for digital display of cultural resources based on NLP technology comprehensively processes the text, image, and video data of cultural resources through NLP technology, extracts text summaries, and generates text descriptions of images and videos, realizing the fusion and classification of multi-modal information. These feature information are matched according to predefined classification rules, the cultural feature matching index is calculated, and the information is classified as matched and unmatched. The matched information is incorporated into the corresponding data set, while the unmatched information is reclassified for processing. User behavior data and geographical location information are obtained in real time, user behavior features and regional cultural features are extracted, the content display index is calculated, and the display order of the cultural resource data set is dynamically adjusted, thereby providing a personalized display effect and improving the user experience and information dissemination efficiency.

Description

Technical Field

[0001] The present invention relates to the field of language and cultural information technology, and specifically to a method for digital display of cultural resources based on NLP technology. Background Art

[0002] With the acceleration of globalization and the development of information technology, the digitalization demand for various cultural resources (such as documents, artworks, historical sites, etc.) is increasing day by day. Digitalization can protect and disseminate these cultural resources, enabling them to be accessed by a wider audience and understood and shared in different cultural backgrounds. However, the diversity and complexity of cultural resources, especially the display in a multilingual and multimodal environment, pose great challenges to the digitalization work.

[0003] For example, the multilingual characteristic cultural resource display system and its display method with the announcement number CN109857858B include a resource information recording device for recording multilingual characteristic cultural resources in the areas along the regional cooperation line, a resource relay device for receiving information from the resource information recording device through wireless communication, a middle-section information classification processing device connected to the resource relay device for classifying the received information, and a characteristic cultural resource display device for receiving the multilingual characteristic cultural resource information sent by the middle-section information classification processing device and displaying it. The present invention classifies the recorded data during the recording process so that different types of data can be transmitted quickly, and the information is displayed in a classified manner, meeting the needs of the audience to obtain multilingual characteristic cultural resource information in the areas along the regional cooperation line, and can match and adjust the display content according to the audience feedback, improving the audience's interest in understanding the multilingual characteristic cultural resource information.

[0004] However, in the above method, the current classification and display methods rely on predefined rules and manual classification, and cannot fully adapt to the complexity and diversity of the content of cultural resources. Especially when facing a large amount of data, the efficiency is low. Cultural resources not only include text information, but may also involve multimodal data such as images, audio, and video. The existing methods are difficult to achieve efficient integration and display of multimodal data, which may lead to fragmentation of the display content or loss of important information. Although the existing methods can adjust the display content according to the audience feedback, the processing of the feedback is relatively simple, and the user interaction data is not fully utilized for in-depth analysis and dynamic adjustment. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method for digital display of cultural resources based on NLP technology, which solves the problems in the above background art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for digital display of cultural resources based on NLP technology, characterized by including the following steps: S1. Extract text information from cultural resource data through NLP to generate multi-modal information of cultural resources, extract features from the multi-modal information of cultural resources to generate cultural resource feature information, and store it in the cultural resource database; S2. Match the cultural resource feature information with the classification rules according to the predetermined classification rules, judge the matching degree of the cultural resource feature information, and classify the cultural resource feature information into feature matching information and feature non-matching information; S3. Classify the cultural resource data corresponding to the feature matching information into the cultural data set that conforms to the classification rules, mark the feature non-matching information and re-classify the features; S4. Real-time obtain user behavior data and geographical location information, extract user behavior characteristics and regional cultural characteristics, obtain the content display index, match the corresponding cultural data set, and dynamically adjust the display order of the cultural data set.

[0007] Further, the specific process of extracting text information from cultural resource data through NLP to generate multi-modal information of cultural resources is as follows: Preprocess and extract the abstract of the text data of cultural resources through NLP natural language processing technology. The preprocessing includes removing noise, word segmentation, and lemmatization to generate text abstract information; Preprocess the text of the picture and video data of cultural resources through NLP natural language processing technology. The text preprocessing includes text extraction, description generation, text abstract processing, and information fusion to generate multi-modal description information of images and videos; Comprehensively process the text abstract information and the multi-modal description information of images and videos to generate multi-modal cultural resource information.

[0008] Further, the specific process of matching the cultural resource feature information with the classification rules according to the predetermined classification rules is as follows: According to the predefined classification rules, preliminarily classify the extracted cultural resource feature information and map it to the corresponding classification labels; The cultural resource feature information includes text abstracts, image, and video description information.

[0009] Further, the process of preliminarily classifying the extracted cultural resource feature information is as follows: Match the cultural resource feature information with the classification rules item by item, calculate the matching degree, and judge whether each feature meets the conditions of the classification rules; Calculate the matching index of each feature to obtain the cultural feature matching index.

[0010] Further, the specific process of determining the matching degree of the cultural resource feature information is as follows: Based on the cultural feature matching index, evaluate the matching degree between the cultural resource feature information and the predefined classification rules; compare the cultural feature matching index with the set cultural feature matching index threshold. When the cultural feature matching index is lower than the cultural feature matching index threshold, it indicates that the cultural resource feature information is feature-mismatched information; when the cultural feature matching index is greater than or equal to the cultural feature matching index threshold, it indicates that the cultural resource feature information is feature-matched information.

[0011] Further, the specific process of obtaining the content display index is as follows: Set the weight value of each cultural resource feature information according to the user behavior data and regional cultural characteristics; perform a matching degree analysis on the cultural resource feature information in the cultural dataset with the user behavior characteristics and regional cultural characteristics through the NLP algorithm to generate a matching degree score; synthesize each matching degree score and combine it with the feature weight to obtain the content display index.

[0012] Further, the specific process of matching the corresponding cultural dataset is as follows: Obtain the content display index of each cultural dataset, and filter out the cultural resource datasets that meet the content display index according to the threshold of the content display index.

[0013] Further, the specific process of dynamically adjusting the display order of the cultural datasets is as follows: Sort the cultural resource datasets that meet the content display index from largest to smallest, update the content display index of each cultural data value in real time, and adjust the display order of the cultural datasets according to the sorting order.

[0014] The present invention has the following beneficial effects:

[0015] (1) The cultural resource digital display method based on the NLP technology extracts and classifies the multimodal information of cultural resources through the NLP technology, ensuring that different types of cultural resources can be uniformly processed and analyzed, providing multi-level information integration capabilities, and enhancing the accuracy and integrity of the display content. Matching and classifying the cultural resource feature information through the predefined classification rules realizes efficient cultural resource management. Through accurate feature matching, the relevance of the display content is ensured, enabling users to obtain the required cultural information more accurately.

[0016] (2) The cultural resource digital display method based on NLP technology classifies and processes matching information and non-matching information, making the cultural resource display process more refined, effectively reducing incorrect displays and information redundancy, and improving the intelligence and reliability of the system. It obtains user behavior data and geographical location information in real time, and dynamically adjusts the display order of cultural resources according to the content display index, realizing personalized and intelligent display methods, enhancing the user's interactive experience, and improving the dissemination efficiency of cultural resources.

[0017] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the cultural resource digital display method based on NLP technology of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The embodiment of the present application solves the problems of the limitations in the prior art of being unable to efficiently process and display multi-modal cultural resource data, as well as dynamically adjusting the display content order and form, through the cultural resource digital display method based on NLP technology, and realizes a more intelligent and personalized display of cultural resources.

[0020] The general idea for the problems in the embodiment of the present application is as follows:

[0021] Extract text information from cultural resource data through NLP to generate multi-modal information of cultural resources, extract features from the multi-modal information of cultural resources to generate feature information of cultural resources, and store it in the cultural resource database.

[0022] Match the cultural resource feature information with the classification rules according to the predetermined classification rules, judge the matching degree of the cultural resource feature information, and classify the cultural resource feature information into feature matching information and feature non-matching information.

[0023] Classify the cultural resource data corresponding to the feature matching information into the cultural data set that conforms to the classification rules, mark the feature non-matching information, and re-classify the features.

[0024] Obtain user behavior data and geographical location information in real time, extract user behavior features and regional cultural features, obtain the content display index, match the corresponding cultural data set, and dynamically adjust the display order of the cultural data set.

[0025] Please refer to Figure 1, an embodiment of the present invention provides a technical solution: a method for digital display of cultural resources based on NLP technology, which is characterized by including the following steps: S1. Extract text information from cultural resource data through NLP to generate multi-modal information of cultural resources, extract features from the multi-modal information of cultural resources to generate cultural resource feature information and store it in the cultural resource database; S2. Match the cultural resource feature information with the classification rules according to the predetermined classification rules, judge the matching degree of the cultural resource feature information, and classify the cultural resource feature information into feature matching information and feature non-matching information; S3. Classify the cultural resource data corresponding to the feature matching information into the cultural data set that conforms to the classification rules, mark the feature non-matching information and re-classify the features; S4. Real-time obtain user behavior data and geographical location information, extract user behavior features and regional cultural features, obtain the content display index, match the corresponding cultural data set, and dynamically adjust the display order of the cultural data set.

[0026] In this implementation, NLP (Natural Language Processing) refers to a technology in the fields of computer science and artificial intelligence used to process and analyze human language, enabling computers to understand, interpret, and generate natural language. Cultural resource data includes cultural information in the forms of text, images, videos, etc., such as literature, artworks, historical sites, etc., which are used to represent and preserve cultural content. Multimodal information refers to information that simultaneously contains multiple data types, such as text, images, videos, etc. For cultural resources, this means processing by integrating text descriptions, image content, and video information. Feature extraction means extracting features from data that can represent its main content for further analysis and classification. In multimodal information, feature extraction may include extracting text summaries, image features, and video content descriptions. Cultural resource feature information refers to the key information obtained during the feature extraction process for describing cultural resources, such as the theme of the text, the main content of the image, the core information of the video, etc. A cultural resource database is a database system for storing and managing cultural resource data and its feature information, facilitating subsequent retrieval and display. Predefined classification rules are used to classify data into different categories. In this method, the classification rules are used to assign cultural resource feature information to predefined categories or data sets. Feature matching information refers to cultural resource feature information that conforms to the classification rules. That is, this information meets the preset classification criteria and can be assigned to the corresponding cultural data set. Feature non-matching information refers to cultural resource feature information that does not conform to the predefined classification rules. Such information needs to be reclassified or further processed to make it conform to the classification criteria. User behavior data represents the data generated by users during their interaction with the digital display system, including click records, browsing history, etc., which is used to analyze users' interests and behavior patterns. Geographic location information refers to the geographic location data of users, which is used to understand users' regional backgrounds and thus provide cultural resources related to their locations. The content display index is an indicator for measuring the content display effect, which is used to evaluate the relevance and attractiveness of the displayed content based on user behavior data and geographic location information. The displayed content is adjusted according to real-time data or feedback information to optimize the display effect and user experience. In this method, the dynamic adjustment changes the display order of the cultural data sets according to the content display index.

[0027] Specifically, the specific process of extracting text information from cultural resource data through NLP to generate multimodal information of cultural resources is as follows: The text data of cultural resources is preprocessed and summarized through NLP (Natural Language Processing) technology. The preprocessing includes removing noise, word segmentation, and lemmatization to generate text summary information; The text preprocessing of the picture and video data of cultural resources is carried out through NLP (Natural Language Processing) technology. The text preprocessing includes text extraction, description generation, text summary processing, and information fusion to generate multimodal description information of images and videos; The text summary information and the multimodal description information of images and videos are comprehensively processed to generate multimodal cultural resource information.

[0028] In this implementation scheme, NLP natural language processing technology is used to process and analyze human language, enabling computers to understand text data. Cleaning the noise in text data includes spelling mistakes, irrelevant content, word segmentation which means splitting the text into words or phrases, and lemmatization which means reducing words to their basic forms to more accurately extract useful information. Extracting core information or main ideas from a large amount of text to generate text summary information. Processing the text information in picture and video data. Extracting the text content from the text part in the picture. Generating text descriptions for images and videos, and the description content includes the main objects, events or scenes in the image or video. Performing summary processing on the generated text descriptions to extract key information. Integrating the text information extracted from different modalities (pictures, videos) to form a comprehensive multi-modal description information, and integrating the information from different modalities (text, image, video) to generate a comprehensive cultural resource description. This comprehensive processing combines the text summary information with the multi-modal description information of images and videos to form a comprehensive description containing rich information, facilitating subsequent classification and display.

[0029] Specifically, the specific process of matching the cultural resource feature information with the classification rules according to the predetermined classification rules is as follows: According to the predefined classification rules, the extracted cultural resource feature information is preliminarily classified and mapped to the corresponding classification labels; the cultural resource feature information includes text summaries, image and video description information.

[0030] In this implementation scheme, the preliminary classification of the cultural resource feature information according to the predefined classification rules is used to classify the cultural resources. These rules can be set based on the theme, type, and formal features of the cultural resources. The extracted cultural resource feature information includes the information extracted from text, images, and videos, such as text summaries, image descriptions, video content descriptions, etc. These feature information need to be organized and classified to meet the display or analysis requirements. The extracted feature information (text summaries, image and video descriptions) is preliminarily classified according to the predefined rules. This means allocating the information to the preset categories, such as "History", "Art", "Nature". These are the labels used in the predefined rules to identify different categories. Each label represents a category or theme, and the cultural resource feature information is classified according to these labels. The main information or core content extracted from the text data of the cultural resources. The text description of the image content, including the objects, scenes or activities in the image. The text description of the video content, including the main scenes, actions or dialogues in the video.

[0031] Specifically, the process of initially classifying the extracted cultural resource feature information is as follows: Match the cultural resource feature information with the classification rules item by item, calculate the matching degree, and determine whether each feature meets the conditions of the classification rules; calculate the matching index of each feature to obtain the cultural feature matching index.

[0032] In this implementation plan, the cultural resource feature information includes text summaries, image descriptions, and video descriptions. These information are extracted from cultural resources for classification. Predefined criteria or rules for classifying cultural resource information into specific categories. The rules can include keywords, topic models, description patterns, etc. The matching degree refers to the degree of similarity or consistency between the feature information and the classification rules. It can be calculated using similarity measurement methods. Based on the calculated matching degree, it is determined whether the feature meets the requirements of the classification rules. The feature may or may not meet the rules, which determines whether it is classified into this category. The matching index is a quantitative indicator measuring the degree of matching between the feature and the classification rules. The matching index can be a numerical value indicating the degree of consistency between the feature and the rules. The cultural feature matching index is obtained as follows, , where CI represents the cultural feature matching index, represents the total number of features, represents the weight of the th feature, represents the th feature and the similarity between the corresponding classification rule feature can be based on the TF-IDF similarity calculation formula, expressed as follows: , represents the TF-IDF value of the th feature in the cultural resource feature information. TF-ID represents the TF-ID value of the th feature in the classification rule. , where, represents the th feature frequency in the document. , where, represents the total number of documents, represents the number of documents containing the feature .

[0033] Specifically, the specific process of determining the matching degree of cultural resource feature information is as follows: Based on the cultural feature matching index, evaluate the matching degree between the cultural resource feature information and the predefined classification rules; compare the cultural feature matching index with the set cultural feature matching index threshold. When the cultural feature matching index is lower than the cultural feature matching index threshold, it indicates that the cultural resource feature information is feature mismatch information; when the cultural feature matching index is greater than or equal to the cultural feature matching index threshold, it indicates that the cultural resource feature information is feature matching information.

[0034] In this implementation plan, calculate the similarity between each cultural resource feature information and the corresponding classification rule, and combine the weights of all features to obtain a comprehensive cultural feature matching index, which quantifies the matching degree between the cultural resource feature information and the classification rule. Compare the calculated cultural feature matching index with the pre-set matching index threshold. If the matching index is lower than the set threshold, it means that the matching degree between the cultural resource feature information and the classification rule is low and cannot meet the classification requirements. At this time, mark this feature information as "feature mismatch information". On the contrary, if the matching index is higher than or equal to the threshold, it means that the matching degree between this feature information and the classification rule is high and meets the classification standard. At this time, mark this feature information as "feature matching information". By comparing with the threshold, the cultural resource feature information can be effectively divided into feature matching information that conforms to the classification rule and feature mismatch information that does not conform to the classification rule. This process ensures that only high-quality cultural resource feature information that is highly consistent with the classification rule will be further processed or used, while the part that does not meet the requirements will be marked and processed.

[0035] Specifically, the specific process of obtaining the content display index is as follows: Set the weight values of each cultural resource feature information according to the user behavior data and regional cultural characteristics; perform a matching degree analysis on the cultural resource feature information in the cultural dataset with the user behavior characteristics and regional cultural characteristics through the NLP algorithm to generate matching degree scores; synthesize the various matching degree scores and combine them with the feature weights to obtain the content display index.

[0036] In this implementation plan, according to the user behavior data and regional cultural characteristics, a weight value is assigned to each cultural resource feature information. The weight value reflects the importance of this feature for content display. For example, if user behavior indicates that they are more inclined to specific types of cultural resources, the weight of the relevant feature information will be higher. Through the NLP algorithm, the matching degree between each cultural resource feature information in the cultural dataset and the user behavior characteristics and regional cultural characteristics is analyzed. This analysis process generates a matching degree score, indicating the similarity between this cultural resource feature and the user needs and regional culture. All the matching degree scores are combined, and combined with the weights of each feature, a content display index is calculated. This index quantifies the adaptability of cultural resources to the current user and regional environment. The higher the index, the more suitable it is to be displayed in the current situation. The calculation formula of the content display index is expressed as: , where DI represents the content display index, which is used to represent the adaptability index of cultural resources during display. Wi represents the weight value of the i-th cultural resource feature information, and the weight value is set based on user behavior data and regional cultural characteristics. represents the matching degree score between the i-th cultural resource feature information and the user behavior characteristics and regional cultural characteristics, and the score is obtained through NLP algorithm analysis. h represents the total number of cultural resource feature information, which refers to the number of features considered in the cultural dataset.

[0037] Specifically, the specific process of matching the corresponding cultural dataset is as follows: Obtain the content display index of each cultural dataset, and filter out the cultural resource datasets that meet the content display index according to the threshold of the content display index.

[0038] In this implementation plan, according to the user behavior data and regional cultural characteristics, the content display index is calculated for each cultural resource dataset. The content display index reflects the matching degree between the cultural resource dataset and the current user needs. Define a threshold as the screening criterion. This threshold is a numerical value, representing the minimum matching degree that the cultural resource dataset needs to reach to ensure its display to users. Compare the content display indexes of all cultural resource datasets with the set threshold. Only those cultural resource datasets whose content display index is greater than or equal to the threshold will be screened out. This means that these datasets meet or exceed the minimum requirements for display and have a higher priority display value.

[0039] Specifically, the specific process of dynamically adjusting the display order of the cultural dataset is as follows: Sort the cultural resource datasets that meet the content display index from largest to smallest, update the content display index of each cultural data value in real time, and adjust the display order of the cultural dataset according to the sorting order.

[0040] In this implementation, all cultural resource datasets that meet the content display index threshold are sorted according to the size of the content display index. The higher the display index, the higher the matching degree of the dataset with the user, and it will be displayed preferentially. As the user's behavior and needs change, the content display index also needs to be updated in real time. The relevance and priority of the cultural resource datasets may change. According to the sorted content display index after updating, the display order of the cultural datasets is adjusted to ensure that the cultural resource datasets that best meet the current user needs can be displayed preferentially. This dynamic adjustment process can improve the user experience and make the displayed content always consistent with the user's interests and needs.

[0041] In summary, this application has at least the following effects:

[0042] The cultural resource digital display method based on NLP technology comprehensively processes the text, image, and video data of cultural resources through NLP technology, extracts text summaries, and generates text descriptions of images and videos to achieve the fusion and classification of multi-modal information. According to the predefined classification rules, these feature information are matched, the cultural feature matching index is calculated, and the information is classified as matching and non-matching. The matching information is included in the corresponding dataset, and the non-matching information is reclassified and processed. The user behavior data and geographical location information are obtained in real time, the user behavior characteristics and regional cultural characteristics are extracted, the content display index is calculated, and the display order of the cultural resource datasets is dynamically adjusted, so as to provide a personalized display effect and improve the user experience and information dissemination efficiency.

[0043] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0045] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction means that implements the functions specified in one or more of the processes and / or blocks Figure 1 of one or more of the processes and / or blocks Figure 1 specified.

[0046] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 of one or more of the processes and / or blocks Figure 1 specified.

[0047] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0048] It is obvious that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A digital display method of cultural resources based on NLP technology, characterized in that: The following steps are involved: S1. Extract text information from cultural resource data through NLP to generate multimodal information of cultural resources, extract features from the multimodal information of cultural resources, generate cultural resource feature information and store it in the cultural resource database; S2. Matching the cultural resource feature information with the classification rules according to the predefined classification rules, determining the matching degree of the cultural resource feature information, and classifying the cultural resource feature information into feature matching information and feature non-matching information; S3. Classify the cultural resource data corresponding to the feature matching information into the cultural data set that conforms to the classification rules, mark the feature mismatch information and reclassify the features; S4. Obtain user behavior data and geographic location information in real time, extract user behavior characteristics and regional cultural characteristics, obtain content display index, match corresponding cultural data sets, and dynamically adjust the display order of cultural data sets; The specific process of obtaining the content display index is as follows: According to user behavior data and regional cultural characteristics, set the weight value of each cultural resource feature information; Through the NLP algorithm, the matching degree of the cultural resource feature information in the cultural data set is analyzed with the user behavior characteristics and regional cultural characteristics to generate a matching score; The content display index is obtained by combining various matching scores and feature weights.

2. The method for digital display of cultural resources based on NLP technology according to claim 1 is characterized in that: The specific process of extracting text information from cultural resource data through NLP and generating multimodal information of cultural resources is as follows: The text data of cultural resources is preprocessed and abstracted through NLP natural language processing technology. The preprocessing includes noise removal, word segmentation and word form restoration to generate text summary information. The text preprocessing of the pictures and video data of cultural resources is carried out through NLP natural language processing technology. The text preprocessing includes text extraction, description generation, text summary processing and information fusion to generate multimodal description information of images and videos. The text summary information and the multimodal description information of images and videos are comprehensively processed to generate multimodal cultural resource information.

3. The method for digital display of cultural resources based on NLP technology according to claim 2 is characterized in that: The specific process of matching cultural resource feature information with classification rules according to predefined classification rules is as follows: According to the predefined classification rules, the extracted cultural resource feature information is preliminarily classified and mapped to corresponding classification labels; The cultural resource feature information includes text summary, image and video description information.

4. The method for digital display of cultural resources based on NLP technology according to claim 3 is characterized by: The process of preliminarily classifying the extracted cultural resource feature information is as follows: Match the cultural resource feature information with the classification rules item by item, calculate the matching degree, and determine whether each feature meets the conditions of the classification rules; Calculate the matching index of each feature and obtain the cultural feature matching index.

5. The method for digital display of cultural resources based on NLP technology according to claim 4 is characterized in that: The specific process of judging the matching degree of cultural resource feature information is as follows: Based on the cultural feature matching index, the matching degree between the cultural resource feature information and the predefined classification rules is evaluated; The cultural feature matching index is compared with the set cultural feature matching index threshold. When the cultural feature matching index is lower than the cultural feature matching index threshold, it indicates that the cultural resource feature information is feature mismatch information. When the cultural feature matching index is greater than or equal to the cultural feature matching index threshold, it indicates that the cultural resource feature information is feature matching information.

6. The method for digital display of cultural resources based on NLP technology according to claim 5 is characterized in that: The specific process of matching the corresponding cultural data set is as follows: The content display index of each cultural data set is obtained, and the cultural resource data sets that meet the content display index are screened out according to the threshold of the content display index.

7. The method for digital display of cultural resources based on NLP technology according to claim 6 is characterized by: The specific process of dynamically adjusting the display order of cultural data sets is as follows: The cultural resource data sets that meet the content display index are sorted from large to small, the content display index of each cultural data value is updated in real time, and the display order of the cultural data sets is adjusted according to the sorting order.

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