A data processing system for obtaining APP types

Abstract

This invention relates to a data processing system for obtaining app types. The system includes: a first database, a second database, a third database, a processor, and a memory storing a computer program. The first database includes an original set of apps, the second database includes a sample set of apps, and the third database includes a set of non-sample apps. When the computer program is executed by the processor, the following steps are implemented: obtaining a target tag set corresponding to the initial app list; obtaining the final tags of the non-sample apps based on the sample app set and the target tag set; and obtaining the app type corresponding to the non-sample apps based on the final tags. This invention provides a novel method for obtaining app types. By applying different processing methods to non-sample apps, tags for all apps are obtained, thereby classifying the apps and achieving higher accuracy in obtaining app types.

Description

Technical Field

[0001] This invention relates to the field of APP processing technology, and in particular to a data processing system for obtaining APP type. Background Technology

[0002] With the popularization and development of smart technology, more and more application software (APP) is appearing on device application platforms. The continuous innovation and development of technology has led to an increasing number of application software types and a growing number of applications within each type. How to clearly classify and process the application software on device application platforms has become a hot research issue. By obtaining APP tags and accurately classifying APPs, more precise services can be provided to users.

[0003] Currently, existing technologies for obtaining APP classification involve: retrieving corresponding description information based on the APP's ID, inputting this description information into a model to generate vectors, retrieving a preset installation sequence based on the APP's ID to generate vectors, and inputting all generated vectors into a classification model to output the APP type. The problems with this method of obtaining APP type are as follows:

[0004] On the one hand, due to the limitations of the method, the simplistic APP tags result in low accuracy in APP classification;

[0005] On the other hand, due to the sheer number of apps, classifying only a specific portion of apps cannot fully and automatically cover all apps, resulting in lower accuracy in the app types obtained. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention adopts the following technical solution: a data processing system for acquiring APP types, the system comprising: a first database, a second database, a third database, a processor, and a memory storing computer programs, wherein the first database includes an original APP set, which is a collection of all APPs across all application platforms; the second database includes a sample APP set, which includes a first sample APP set and a second sample APP set; and the third database includes a non-sample APP set, which is a collection of APPs from the original APP set excluding the sample APP set. When the computer program is executed by the processor, the following steps are implemented:

[0007] S10, Obtain the first non-sample APP list FA = {FA1, ..., FA2} from the non-sample APP set. α , ..., FA λ The second non-sample APP list FB = {FB1, ..., FB2} β ..., FB γ},FA α For the α-th non-sample APP information, α = 1...λ, where λ is the number of non-sample APPs. FB β Let β be the information of the second non-sample APP, where β = 1...γ, and γ is the number of second non-sample APPs.

[0008] S20, Based on the first sample APP set, obtain the initial APP list B = {B1, ..., B...} i , ..., B m The initial text list C corresponding to B is {C1, ..., C...} i , ..., C m The initial pkg list Q = {Q1, ..., Q} corresponding to B and B is Q = {Q1, ..., Q}. i Q m}, B i Let C be the name of the i-th initial app. i For B i The corresponding initial APP text, Q i For B i The corresponding pkg name, i = 1...m, where m is the initial number of apps.

[0009] S30, Based on B and C, obtain the target label set D' = {D'1, ..., D''} corresponding to B. i , ..., D' m},D' i For B i The corresponding list of target tags.

[0010] S40, according to FA α and B i , obtain FA α The corresponding final tag list.

[0011] S50, according to FB β and Q i Get FB β The corresponding final tag list.

[0012] S60, according to FA α The corresponding final tag list and FB β The corresponding final tag list, obtain FA α Corresponding APP types and FB β The corresponding APP type.

[0013] Compared with the prior art, the present invention has significant advantages. Through the above technical solution, the data processing system for acquiring APP types provided by the present invention achieves considerable technological progress and practicality, and has broad industrial application value. It has at least the following advantages:

[0014] This invention provides a data processing system for obtaining APP types. The system includes: a first database, a second database, a third database, a processor, and a memory storing a computer program. The first database includes an original APP set, which is a collection of all APPs across all application platforms. The second database includes a sample APP set, which includes a first sample APP set and a second sample APP set. The third database includes a non-sample APP set, which is a collection of APPs from the original APP set excluding the sample APP set. When the computer program is executed by the processor, the following steps are implemented: obtaining a first non-sample APP list and a second non-sample APP list from the non-sample APP set; obtaining a target tag list corresponding to the initial APP based on the initial APP list obtained from the first sample APP set and the corresponding initial text list; obtaining the final tag of the first non-sample APP based on the first non-sample APP and the initial APP; obtaining the final tag of the second non-sample APP based on the second non-sample APP and the initial APP; and obtaining the APP type corresponding to the non-sample APP based on the final tags of the first and second non-sample APPs. As can be seen, on the one hand, the present invention obtains sample labels through model training and processes the sample labels to complicate the APP labels, thereby making the obtained APP labels more accurate; on the other hand, it is not limited to classifying specific parts of APPs, but can fully and automatically cover all APPs by using different processing methods for APPs, thereby making the obtained APP types more accurate. Attached Figure Description

[0015] Figure 1 This is a flowchart of an execution computer program for a data processing system that obtains APP type data, provided as an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] This invention provides a data processing system for obtaining APP types. The system includes: a first database, a second database, a third database, a processor, and a memory storing a computer program. The first database includes an original APP set, which is a collection of all APPs across all application platforms. The second database includes a sample APP set, which includes a first sample APP set and a second sample APP set. The third database includes a non-sample APP set, which is a collection of APPs from the original APP set excluding the sample APP set. When the computer program is executed by the processor, the following steps are implemented: Figure 1 As shown:

[0019] S10, Obtain the first non-sample APP list FA = {FA1, ..., FA2} from the non-sample APP set. α , ..., FA λ The second non-sample APP list FB = {FB1, ..., FB2} β ..., FB γ},FA α For the α-th non-sample APP information, α = 1...λ, where λ is the number of non-sample APPs. FB β Let β be the information of the second non-sample APP, where β = 1...γ, and γ is the number of second non-sample APPs.

[0020] Specifically, the initial text is the text used to describe the initial APP.

[0021] Specifically, the first non-sample APP information includes the name of the first non-sample APP but does not include the APP text and pkg name of the first non-sample APP.

[0022] Specifically, the second non-sample APP information includes the pkg name of the second non-sample APP but does not include the APP text of the second non-sample APP or the name of the second non-sample APP.

[0023] S20, Based on the first sample APP set, obtain the initial APP list B = {B1, ..., B...} i , ..., B m The initial text list C corresponding to B is {C1, ..., C...} i , ..., C m The initial pkg list Q = {Q1, ..., Q} corresponding to B and B is Q = {Q1, ..., Q}. i Q m}, B i Let C be the name of the i-th initial app. i For B i The corresponding initial APP text, Q i For B i The corresponding pkg name, i = 1...m, where m is the initial number of apps.

[0024] Specifically, S20 also includes the following steps:

[0025] S210, Obtain the first sample APP set H = {H1, ..., H2} j H n}, H j ={H j1 H jy H jq(j)}, H jy Let y be the information of the first sample APP corresponding to the j-th APP application platform, j = 1...n, where n is the number of APP application platforms, and y = 1...q(j), where q(j) is the number of first sample APPs corresponding to the j-th APP application platform. The information of the first sample APP includes the name of the first sample APP and the APP text of the first sample APP.

[0026] S220, iterate through H and retrieve H from H. jy Corresponding key APP list U jy ={U 1 jy , ..., U e jy , ..., U f jy}, where U e jy For H jyThe corresponding e-th key APP information, e = 1...f, where f is the number of key APPs, wherein the key APP information includes the key APP name and the APP text of the key APP, wherein each U e jy Key APP names and H jy The first sample app had the same name.

[0027] S230, according to U jy , get U jy The corresponding list of text character counts SL jy ={SL 1 jy , ...,SL e jy , ...,SL f jy}, SL e jy For U e jy The number of characters in the corresponding APP text.

[0028] S240, traverse SL jy And when SL e jy For SL jy When determining the maximum number of characters in the text of an app, U e jy Insert value B.

[0029] The above method, by obtaining the text with the largest number of characters corresponding to the key APP, makes the obtained APP information more accurate, effectively determines the APP name and corresponding APP text, and avoids the same APP being described with different texts due to different APP application platforms.

[0030] S30, Based on B and C, obtain the target label set D' = {D'1, ..., D''} corresponding to B. i , ..., D' m},D' i For B i The corresponding list of target tags.

[0031] Specifically, S30 also includes the following steps:

[0032] S310, according to B i and C i , obtain B i The corresponding target word vector list D i ={B 0 i C 0 i1, ……, C 0 ix , ……, C 0 ip(i)},B 0 i is B i the corresponding first word vector, C 0 ix is C i the x-th second word vector corresponding to C, x = 1 …… p(i), p(i) is the number of second word vectors corresponding to C i the number of corresponding second word vectors.

[0033] In a specific embodiment, before S310, the following steps are further included:

[0034] S3101, perform word segmentation on C i to obtain the first initial word list corresponding to C, where those skilled in the art know that any word segmentation method in the prior art can be adopted according to actual needs, which will not be elaborated here. i

[0035] S3103, delete the preset words in the preset word list from the first initial word list corresponding to C, and obtain the second initial word list corresponding to C; where those skilled in the art know that the preset word list is set according to actual needs. For example, the preset words are "de", "děi", etc., which will not be elaborated here. i i

[0036] S3105, input any second initial word in the second initial word list corresponding to C into the word embedding model to obtain the second word vector. i

[0037] Furthermore, the vector dimensions of B i and C i are the same. Those skilled in the art know that any word embedding model is determined according to actual needs to obtain the word vector corresponding to the initial APP name and the word vector corresponding to any word in the initial APP text, which will not be elaborated here.

[0038] S320, input D i into the preset first neural network model to obtain the first intermediate label list D i corresponding to B 0 i .

[0039] Specifically, the sample APP set further includes a second sample APP set.

[0040] In a specific embodiment, the first neural network model is obtained through the following steps in S320:

[0041] S3201, Based on the second sample APP set, obtain the first sample word vector corresponding to the second sample APP set, wherein each second sample APP information in the second sample APP set includes: the second sample APP name, the APP text of the second sample APP name, and the APP tag list corresponding to the second sample APP name.

[0042] Specifically, the method for obtaining the first sample word vector is the same as the method for obtaining the target word vector.

[0043] As mentioned above, by using the same method to obtain word vectors, it can be ensured that the obtained sample word vectors and target word vectors are represented in the same way, providing effective data for subsequent APP classification and making the obtained APP type more accurate.

[0044] S3203, the first sample word vector set corresponding to the second sample APP set is used as a training dataset and input into a preset first initial neural network model to obtain the sample label set corresponding to the second sample APP set. The sample label set includes a list of first sample labels corresponding to the names of the second sample APPs. The process of training a neural network model is known to those skilled in the art and will not be described in detail here.

[0045] S3205, obtain the first neural network model based on the first sample label list corresponding to the second sample APP name and the APP label list corresponding to the second sample APP name.

[0046] The above describes how a neural network model is trained using the APP name and APP text to obtain the first neural network model, which can accurately determine the APP tag and thus accurately obtain the APP type without text description.

[0047] S330, when D 0 i When =Null, B i The corresponding initial APP is used as the first APP.

[0048] S340, when D 0 i ≠Null and K 0 i ≥K 0 At that time, B i The corresponding initial APP is used as the first APP, where K 0 i D 0 i The corresponding number of first intermediate tags, K 0 This is a preset threshold for the number of tags.

[0049] Specifically, the preset tag quantity threshold K 0 The value range of K is 1 to 3, where, as those skilled in the art know, K is determined according to actual needs. 0 The selection of [specific element] will not be elaborated upon here.

[0050] As mentioned above, for apps with an excessive number of tags obtained from the first neural network model, the app tags are further determined to obtain more accurate tags, thereby accurately identifying the app type without text description.

[0051] S350, when D 0 i ≠Null and K 0 i <K 0 At that time, B i The corresponding initial app is used as the second app.

[0052] The above method can accurately determine the initial app tag of an app by using the initial app name and initial app text, and then accurately obtain the app type without text description based on the initial app tag.

[0053] S360, perform a first marking process on the first APP to obtain the target tag corresponding to the first APP.

[0054] Specifically, the first tagging process involves tagging the APP by a tagger.

[0055] S370, perform a second marking process on the second APP to obtain the target tag corresponding to the second APP.

[0056] Specifically, S370 also includes the following steps:

[0057] S3701, Based on the first intermediate tag list corresponding to any second APP, obtain the second intermediate tag list G = {G1, ..., G...} corresponding to the second APP. r , ..., G s}, G r Let r be the r-th intermediate tag corresponding to the second APP, where r = 1...s, and s is the number of intermediate tags corresponding to the second APP.

[0058] S3703, Get the preset APP tag list G 0 ={G 0 1, ..., G 0 t , ..., G 0 g}, G 0t Let t be the t-th preset APP tag, t = 1...g, where g is the number of preset APP tags, and the preset APP tags are the preset tags corresponding to the APPs that represent virtual reality.

[0059] S3705, when G r ≠G 0 t When the first intermediate tag corresponding to the second APP is used as the target tag corresponding to the second APP.

[0060] S3707, when G r =G 0 t At that time, the target word vector list corresponding to the second APP is input into the preset second neural network model to obtain the third intermediate label list corresponding to the second APP.

[0061] In a specific embodiment, the second neural network model is obtained in S3707 through the following steps:

[0062] S37071, Based on the third sample APP set, obtain the second sample word vector corresponding to the third sample APP set, wherein the sample APP set also includes the third sample APP set, and the information of each third sample APP in the third sample APP set includes: the third sample APP name, the APP text of the third sample APP name and the APP tag list corresponding to the third sample APP name, wherein the third sample APP is an APP representing virtual reality.

[0063] Specifically, the method for obtaining the second sample word vector is the same as the method for obtaining the target word vector.

[0064] S37073, the second sample word vectors corresponding to the third sample APP set are used as training datasets and input into a preset second initial neural network model to obtain the second sample label set corresponding to the third sample APP set. The second sample label set includes a list of second sample labels corresponding to the names of the third sample APPs. Those skilled in the art know the process of training a neural network model, and it will not be described in detail here.

[0065] S37075, Based on the second sample label list corresponding to the third sample APP name and the APP label list corresponding to the third sample APP name, obtain the second neural network model.

[0066] S3709, take the third intermediate tag in the third intermediate tag list corresponding to the second APP as the target tag corresponding to the second APP.

[0067] As mentioned above, for apps representing virtual reality, labels need to be obtained through separate neural network models. This avoids the inability to accurately determine the labels of such apps due to the use of the same neural network model, which would lead to the inability to accurately obtain the app type without text description. By further labeling virtual reality apps, more accurate labels can be generated, thereby accurately obtaining the app type without text description.

[0068] S380, Based on the target tags corresponding to the first APP and the second APP, obtain the target tag set D' = {D'1, ..., D''} corresponding to B. i , ..., D' m},D' i For B i The corresponding list of target tags.

[0069] The above method accurately obtains the app's tags through the app name and app text, enabling the app to be categorized based on the app's tags, thus giving most apps on the app application platform tags.

[0070] S40, according to FA α and B i , obtain FA α The corresponding final tag list.

[0071] Specifically, S40 also includes the following steps:

[0072] S410, Based on FA and B, obtain the first similarity list F = {F1, ..., F2} corresponding to FA. α , ..., F λ},F α ={F 1 α , ..., F i α , ..., F m α}, F i α For FA α and B i The first similarity between them.

[0073] Specifically, the following steps are included before S410:

[0074] S4101, Obtain FA α The corresponding first intermediate string.

[0075] S4103, Traverse FA α The corresponding first intermediate string and FA αRemove non-Chinese characters from the corresponding first intermediate string and obtain FA. α The corresponding second intermediate string.

[0076] S4105, according to FA α The corresponding second intermediate string, obtain FA. α The corresponding first intermediate word vector.

[0077] S4107, according to FA α The corresponding first intermediate word vector and B 0 i , obtain F i α Those skilled in the art can determine the method for obtaining the similarity between vectors based on actual needs, such as cosine similarity between vectors, which will not be elaborated here.

[0078] The above steps involve removing non-Chinese words from the app to avoid affecting the accuracy of the similarity between app names. This allows for the accurate identification of such app tags, enabling the accurate acquisition of app types without text descriptions.

[0079] S430, when F i α The highest first similarity and F i α ≥F 0 At 1 o'clock, D' i As FA α The corresponding final tag list, where F 0 1 represents the preset first similarity threshold.

[0080] Specifically, the preset first similarity threshold F 0 The value of 1 ranges from 0.8 to 0.9. Those skilled in the art can set the first similarity threshold according to actual needs, which will not be elaborated here.

[0081] S450, when F i α The highest first similarity and F i α <F 0 At 1 o'clock, according to B i and C i , obtain FA α The corresponding final tag list.

[0082] Specifically, S450 also includes the following steps:

[0083] S4501, obtain C i The corresponding second initial word list.

[0084] S4503, according to C i The corresponding second initial word list, obtain C i The priority YX of any keyword in the corresponding second initial word list v i ,in, Where, η v i C i The vth second initial word in the corresponding second initial word list is in C i The number of times it appears in λ v i C i The total number of times the v-th second initial word in the corresponding second initial word list appears in B and C, μ v i For in D' i In the corresponding target label B i The quantity.

[0085] S4507, when YX v i ≥YX 0 At that time, YX v i The corresponding second initial word is D' i The keywords, among which, YX 0 The priority threshold for keywords is known to those skilled in the art. The priority threshold for keywords is set according to actual needs, and will not be elaborated here.

[0086] S4509, according to D' i Keywords and FA α The similarity between them is used to obtain the FA. α The corresponding final tag list; can be understood as: D' i Keywords transformed into D' i The keyword vector, based on D' i Keyword vectors and FA α Obtain D' from the corresponding first intermediate word vector. i Keywords and FA α The similarity between them, when D' i Keywords and FA α When the similarity between them is not less than the first similarity threshold, based on D' i Construct FA by finding all target labels corresponding to the keywords in D'. α The corresponding final tag list.

[0087] The above approach avoids the inability to obtain tags for apps without text descriptions when the app name cannot be matched, thus improving the accuracy and scope of obtaining tags for apps without text descriptions.

[0088] S50, according to FB β and Q i Get FB β The corresponding final tag list.

[0089] Specifically, S50 also includes the following steps:

[0090] S510, Based on FB and Q, obtain the second similarity list F' = {F'1, ..., F''} corresponding to FB. β , ..., F' γ},F' β ={F' 1 β , ..., F' i β , ..., F' m β}, F' i β For FB β and Q i The second similarity between them.

[0091] Specifically, the method for obtaining the second similarity list is the same as the method for obtaining the first similarity list.

[0092] S530, when F' i β The second-highest similarity and F' i β ≥F 0 At 2 o'clock, D' i As FB β The corresponding final tag list, where F 0 2 is the preset second similarity threshold.

[0093] Specifically, the preset second similarity threshold F 0 The value of 2 ranges from 0.8 to 0.9. Those skilled in the art can set a second similarity threshold according to actual needs, which will not be elaborated here.

[0094] S550, when F i α The second highest similarity and F i α <F 0 At 2 o'clock, no FB was generated. β The corresponding final tag list.

[0095] In another specific embodiment, the non-sample APP set further includes a third non-sample APP list, wherein the third non-sample APP information includes the name of the third non-sample APP and the pkg name of the third non-sample APP, but does not include the APP text. When the computer program is executed by the processor, the following steps are implemented:

[0096] S1. Based on the name of the third non-sample APP and B, determine the final tag list corresponding to the third non-sample APP. The method for determining the final tag list corresponding to the third non-sample APP based on the name of the third non-sample APP and B is the same as that in S40, and will not be described again here.

[0097] S2, when the name of the third non-sample APP is the same as D' i When the similarity between keywords is less than the first similarity threshold, the final tag list corresponding to the third non-sample APP is determined based on the pkg name and Q of the third non-sample APP. The method for determining the final tag list corresponding to the third non-sample APP based on the pkg name and Q of the third non-sample APP is the same as that in S50, and will not be described again here.

[0098] As mentioned above, by using different processing methods for different apps, it is possible to fully and automatically cover all apps, resulting in a high degree of accuracy in obtaining app types.

[0099] S60, according to the FA α The corresponding final tag list and the FB β The corresponding final tag list, obtain FA α Corresponding APP types and FB β The corresponding APP type.

[0100] The above describes how, by obtaining tags for apps with text descriptions, apps without text descriptions can be identified. Based on these tags, apps on the app application platform can be accurately categorized, allowing more apps to be classified into more accurate types and making it easier for users to find them.

[0101] This invention provides a data processing system for obtaining APP types. The system includes: a first database, a second database, a third database, a processor, and a memory storing a computer program. The first database includes an original APP set, which is a collection of all APPs across all application platforms. The second database includes a sample APP set, which includes a first sample APP set and a second sample APP set. The third database includes a non-sample APP set, which is a collection of APPs from the original APP set excluding the sample APP set. When the computer program is executed by the processor, the following steps are implemented: obtaining a first non-sample APP list and a second non-sample APP list from the non-sample APP set; obtaining a target tag list corresponding to the initial APP based on the initial APP list obtained from the first sample APP set and the corresponding initial text list; obtaining the final tag of the first non-sample APP based on the first non-sample APP and the initial APP; obtaining the final tag of the second non-sample APP based on the second non-sample APP and the initial APP; and obtaining the APP type corresponding to the non-sample APP based on the final tags of the first and second non-sample APPs. As can be seen, on the one hand, the present invention obtains sample labels through model training and processes the sample labels to classify APPs more accurately, resulting in higher accuracy of the obtained APP labels; on the other hand, it is not limited to classifying specific parts of APPs, but can fully and automatically cover all APPs by using different processing methods for APPs, resulting in higher accuracy of the obtained APP types.

[0102] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. An acquisition APP type data processing system, characterized by, The system comprises a first database, a second database, a third database, a processor and a memory storing a computer program, wherein the first database comprises an original APP set, the original APP set is a set of all APPs in all application platforms, the second database comprises a sample APP set, the sample APP set comprises a first sample APP set, the third database comprises a non-sample APP set, the non-sample APP set is a set of APPs in the original APP set except the sample APP set, and when the computer program is executed by the processor, the following steps are implemented. S10, obtaining a first non-sample APP list FA = {FA1, …, FA α , …, FA λ} and a second non-sample APP list FB = {FB1, …, FB β , …, FB γ} from the non-sample APP set, FA α is the αth first non-sample APP information, α = 1 … λ, λ is the number of first non-sample APPs, FB β is the βth second non-sample APP information, β = 1 … γ, γ is the number of second non-sample APPs; S20, according to the first sample APP set, obtaining an initial APP list B = {B1, …, B i , …, B m}, the initial text list C = {C1, …, C i , …, C m} corresponding to B and the initial pkg list Q = {Q1, …, Q i , …, Q m} corresponding to B i , B i is the i-th initial APP name, C i is the initial APP text corresponding to B i , Q i is the pkg name corresponding to B, i = 1 … m, and m is the number of initial APPs; S30, according to B and C, obtaining the target label set D' corresponding to B, D' = {D'1,..., D'N} i ,..., D'N} m} i is the target label list corresponding to B i ; S40, according to FA α and B i , obtaining FA α the corresponding final label list; in S40 also comprises the following steps: S410, according to FA and B, obtaining the first similarity list F corresponding to FA={F1, …, F α , λ}, F α ={F 1 α , i α , m α}, F i α is the first similarity between FA α and B i ; S430, when F i α The highest first similarity and F i α ≥F 0 At 1 o'clock, D' i As FA α The corresponding final tag list, where F 0 1 represents the preset first similarity threshold; S450, when F i α is the maximum first similarity and F i α <F 0 1, according to B i and C i , the final label list corresponding to FA α is obtained; the following steps are also included in S450: S4501, obtaining C i a corresponding second initial word list; S4503, according to C i The corresponding second initial word list, obtain C i The priority YX of any keyword in the corresponding second initial word list v i ,in, Where, η v i C i The vth second initial word in the corresponding second initial word list is in C i The number of times it appears in λ v i C i The total number of times the v-th second initial word in the corresponding second initial word list appears in B and C, μ v i For in D' i In the corresponding target label B i Quantity; S4507, when YX v i ≥YX 0 , YX v i the corresponding second initial word as the key word of D' i , wherein YX 0 is the priority threshold of the key word; S4509, according to D i the similarity between the keywords of FA α and the final label list corresponding to FA α is obtained; S50, according to FB β and Q i , obtaining FB β the corresponding final label list; in S50 also comprises the following steps: S510, obtaining a second similarity list F'={F'1,..., F'N} corresponding to FB according to FB and Q, where F'N is a second similarity between FB and Q. β γ β 1 β i β m β i β β i ​​​​​​​​​ S530, when F i β is the maximum second similarity and F i β ≥ F 0 2, D i is taken as FB β , and the corresponding final label list is output, where F 0 2 is a preset second similarity threshold value. S550, when F i α is the maximum second similarity and F i α <F 0 2, FB β the corresponding final label list; S60, according to the FA α corresponding final tag list and the FB β corresponding final tag list, the FA is acquired α corresponding APP type and the FB β corresponding APP type.

2. The data processing system of claim 1, wherein, The first non-sample APP information comprises a first non-sample APP name and does not comprise APP text of the first non-sample APP and a pkg name of the first non-sample APP.

3. The data processing system of the APP type according to claim 2, characterized in that, The second non-sample APP information comprises a pkg name of a second non-sample APP and does not comprise APP text of the second non-sample APP and a name of the second non-sample APP.

4. The data processing system of claim 1, wherein, The step S30 further comprises the following steps: S310, according to B i and C i , obtaining B i The corresponding target word vector list D i ={B 0 i , C 0 i1 , …, C 0 ix , …, C 0 ip(i)}, B 0 i is the corresponding first word vector of B i , C 0 ix is the corresponding xth second word vector of C i , x=1……p(i), p(i) is the number of second word vectors corresponding to C i . S320, the D i input into a preset first neural network model, obtaining B i corresponding first intermediate label list D 0 i ; S330, when D 0 i = Null, B i the corresponding initial APP as the first APP; S340, when D 0 i ≠ Null and K 0 i ≥ K 0 , B i corresponding to the initial APP is taken as the first APP, wherein K 0 i is the D 0 i corresponding to the first intermediate label quantity, and K 0 is a preset label quantity threshold. S350, when D 0 i ≠ Null and K 0 i < K 0 , B i corresponding initial APP as the second APP; S360, performing first label processing on the first APP to obtain a target label corresponding to the first APP; S370, performing second label processing on the second APP to obtain a target label corresponding to the second APP; S380, obtaining a target label set D'={D'1,..., D'K} corresponding to B according to the target label corresponding to the first APP and the target label corresponding to the second APP. i ,..., D' m}, and D' i is a target label list corresponding to B. i ​ 5. The data processing system of the APP type according to claim 4, characterized in that, The step S310 further comprises the following steps: S3101, to C i word segmentation processing, obtaining C i corresponding first initial word list; S3103, from C i The corresponding first initial word list deletes the preset word in the preset word list, and obtains C i The corresponding second initial word list; S3105, the C i Any second initial word in the corresponding second initial word list is input into the word embedding model to obtain a second word vector.

6. The data processing system of claim 1, wherein, The step S410 further comprises the following steps: S4101, obtaining the FA α a corresponding first intermediate string; S4103, traversing the FA α corresponding to the first intermediate string and deleting non-Chinese characters in the corresponding first intermediate string to obtain the FA α corresponding to the first intermediate string and deleting non-Chinese characters in the corresponding first intermediate string to obtain the FA α corresponding to the first intermediate string and deleting non-Chinese characters in the corresponding first intermediate string to obtain the FA S4105, according to the FA α corresponding second intermediate string, obtaining the FA α corresponding first intermediate word vector; S4107, according to FA α corresponding first intermediate word vector and B 0 i , obtaining F i α .

7. The data processing system of the APP type according to claim 1, characterized in that, The non-sample APP set further comprises a third non-sample APP list, wherein the third non-sample APP information comprises a third non-sample APP name and a pkg name of the third non-sample APP and does not comprise APP text, and when the computer program is executed by the processor, the following steps are implemented: S1, determining a final label list corresponding to the third non-sample APP according to the third non-sample APP name and B, wherein the method of determining the final label list corresponding to the third non-sample APP according to the third non-sample APP name and B is consistent with that of S40; S2, when the name of the third non-sample APP is the same as D' i When the similarity between keywords is less than the first similarity threshold, the final tag list corresponding to the third non-sample APP is determined based on the pkg name of the third non-sample APP and Q. The method for determining the final tag list corresponding to the third non-sample APP based on the pkg name of the third non-sample APP and Q is the same as S50.

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