A method and system for intelligent planning of display terminal content based on big data feedback

Abstract

The present invention discloses a method and system for intelligent planning of display terminal content based on big data feedback, which relates to the field of display content services, including: obtaining user access information of all display terminals associated with accounts; synchronizing user access information of different display terminals associated with accounts, and based on the synchronized user access information, comprehensively analyzing and determining the overall content style characteristics of the user; analyzing and determining the individual content style characteristics of the user for each display terminal based on the user access information after separate analysis and processing; constructing a recommended content library for each display terminal; and based on the recommended content library for each display terminal, performing intelligent recommendation planning of content for the corresponding display terminal. The advantages of the present invention are: effectively ensuring that the user can achieve the adaptability of the recommended content and the display terminal while receiving the content of interest, greatly improving the user's experience.

Description

Technical Field

[0001] The present invention relates to the technical field of display content service, and in particular to a method and system for intelligently planning display terminal content based on big data feedback. Background Art

[0002] A display terminal refers to a terminal device used to display information in a communication system. It can be used to display information about the communication system, such as signal status, transmission rate, etc. It can also be used to display the text or image content being sent. Now, more and more terminal products are combined with browser functions, which not only provide browsing functions, but also actively provide information to users. By providing users with content of interest, users can have a better user experience and become more sticky.

[0003] However, the prior art lacks adaptive adjustment for content recommendations for users on different display terminals. When integrating information, only a simple integration analysis is performed on the access information of the same account on different display terminals. As a result, when recommending content, the content often does not match the display terminal. For example, content adapted for mobile phones is recommended on TV, affecting the user experience. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for intelligent planning of display terminal content based on big data feedback are provided. This technical solution solves the above-mentioned problem that when recommending content, the content does not match the display terminal, affecting the user experience.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for intelligent planning of display terminal content based on big data feedback, comprising:

[0007] Obtain user access information of all display terminals associated with the account;

[0008] Synchronize user access information of different display terminals associated with the account, and determine the overall content style characteristics of the user through comprehensive analysis based on the synchronized user access information;

[0009] Based on individually analyzing and processing user access information of each display terminal associated with the account, and based on the individually analyzed and processed user access information, analyzing and determining the individual content style characteristics of the user for each display terminal;

[0010] Building a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal;

[0011] Based on the recommended content library of each display terminal, intelligent content recommendation planning for the corresponding display terminal is performed.

[0012] Preferably, the synchronous processing of user access information of different display terminals associated with accounts specifically includes:

[0013] Based on unique screening of user identity information in each user's access information;

[0014] Synchronize user access information of display terminals with the same user identity information to obtain an access information library corresponding to the user identity information;

[0015] Adding a timestamp to user access information based on the access time of each user access information;

[0016] Determine an information synchronization statistical cycle, and determine the synchronization processing time based on the information synchronization statistical cycle;

[0017] All user access information with a timestamp after the synchronization processing time in the access information database corresponding to the user identity information is screened out and recorded as the user access information after synchronization processing.

[0018] Preferably, the comprehensive analysis based on the synchronously processed user access information to determine the user's overall content style characteristics specifically includes:

[0019] Determine the style feature matrix A of each synchronized user access information i , A i =[a i1 … a ij …a im ], where a ij is the j-th style feature value of the user access information after the i-th synchronization processing, and m is the total number of style features;

[0020] Determine the standardized access index of each synchronized user access information;

[0021] Based on the style feature matrix of each synchronized user access information and the standardized access index of each synchronized user access information, the user's overall content style feature matrix B is constructed, B = [b1 … b j … b m ], where b j is the j-th style feature value of the user;

[0022] in:

[0023]

[0024] In the formula, ε iis the standardized access index of the user access information after the ith synchronization process, and n is the total number of user access information after the synchronization process.

[0025] Preferably, the step of determining the standardized access index of each synchronized user access information specifically includes:

[0026] Determine an access quantification parameter based on the attribute of the user access information, where the access quantification parameter is any one of access duration and access times;

[0027] Determine a parameter value of an access quantization parameter of each synchronized user access information;

[0028] Based on the parameter value of the access quantization parameter of each synchronized user access information, a standardized access index of each synchronized user access information is calculated by a standardized processing formula;

[0029] The standardization processing formula is specifically:

[0030]

[0031] Where, L i is the parameter value of the access quantization parameter of the i-th user access information after synchronization processing.

[0032] Preferably, the determining of the user's individual content style characteristics for each display terminal specifically includes:

[0033] Filter out a number of user access information corresponding to the display terminal from the synchronously processed user access information, and record them as individual access information;

[0034] Based on the style feature matrix of each individual access information and the standardized access index of each individual access information, the user's individual content style feature matrix C for the display terminal is constructed, C = [c1 … c j … c m ], where c j is the j-th style feature value of the user for the display terminal;

[0035] in:

[0036]

[0037] In the formula, ε x is the standardized access index of the xth individual access information, y is the total number of individual access information, a xj The j-th style feature value of the x-th individual access information.

[0038] Preferably, the step of constructing a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal specifically includes:

[0039] Determine the style feature matrix D of each content in the current database, D = [d1 … d j …d m ], where d j is the j-th style feature value of each content in the database;

[0040] Based on the style feature matrix of each content in the current database, the overall content style feature of the user and the individual content style feature of the user for each display terminal, the recommendation index of each content in the database for each display terminal of the user is calculated by an adaptation formula;

[0041] Filter out a number of contents whose recommendation index is less than a preset value to form a recommended content library of the display terminal;

[0042] The adaptation formula is specifically:

[0043]

[0044] Where S is the recommendation index of the content to the user's display terminal, and γ is the individual style correction value of the display terminal.

[0045] Preferably, the intelligent content recommendation planning for the corresponding display terminal based on the recommended content library of each display terminal specifically includes:

[0046] According to the recommendation index of the content in the recommended content library of the display terminal, a priority is added to each content in the recommended content library in ascending order;

[0047] Intelligently recommend content to corresponding display terminals in order of priority.

[0048] Furthermore, a display terminal content intelligent planning system based on big data feedback is proposed, which is used to implement the above-mentioned display terminal content intelligent planning method based on big data feedback, including:

[0049] An information collection module, the information collection module is used to obtain user access information of all display terminals associated with the account;

[0050] An information preprocessing module, the information preprocessing module is electrically connected to the information acquisition module, and the information preprocessing module is used to synchronously process all user access information collected by the information acquisition module to obtain the synchronously processed user access information, and at the same time classify all user access information collected by the information acquisition module according to different display terminals;

[0051] An overall style analysis module, the overall style analysis module is electrically connected to the information preprocessing module, and the overall style analysis module is used to comprehensively analyze and determine the overall content style characteristics of the user based on the synchronously processed user access information;

[0052] An individual style analysis module, the individual style analysis module is electrically connected to the information preprocessing module, and the individual style analysis module is used to analyze and determine the individual content style characteristics of the user for each display terminal based on the user access information that has been individually analyzed and processed;

[0053] A comprehensive planning module, the comprehensive planning module is electrically connected to the overall style analysis module and the individual style analysis module, and is used to construct a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal, and based on the recommended content library for each display terminal, perform intelligent content recommendation planning for the corresponding display terminal.

[0054] Optionally, the overall style analysis module includes:

[0055] An information processing unit, the information processing unit is used to determine a style feature matrix of each synchronously processed user access information and determine a standardized access index of each synchronously processed user access information;

[0056] The overall style fitting unit is used to construct the overall content style feature matrix of the user based on the style feature matrix of each synchronously processed user access information and the standardized access index of each synchronously processed user access information.

[0057] Optionally, the individual style analysis module includes:

[0058] An information screening unit, the information screening unit being electrically connected to the information processing unit, and configured to screen out a plurality of user access information corresponding to the display terminal, recorded as individual access information, and obtain a style feature matrix of the individual access information and a standardized access index of the individual access information from the information processing unit;

[0059] The individual style fitting unit is used to construct an individual content style feature matrix of the user for the display terminal based on the style feature matrix of each individual access information and the standardized access index of each individual access information.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The present invention proposes an intelligent planning scheme for display terminal content based on big data feedback, which determines the user's overall content style characteristics by comprehensively analyzing all the user's access information on all types of display terminals, takes the overall content style characteristics as the basis for content recommendation, and analyzes the user's usage habits on different display terminals in combination with the user's access information on each different display terminal, and recommends content to the user on different display terminals. In this way, it can effectively ensure that the user can receive the content of interest while achieving the compatibility of the recommended content with the display terminal, thereby ensuring that the user can receive the appropriate display content on the appropriate display terminal, which greatly improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Flowchart of the intelligent planning method for display terminal content based on big data feedback proposed in this solution;

[0063] Figure 2 A flowchart of a method for synchronizing user access information of different display terminals associated with accounts in this solution;

[0064] Figure 3 A flow chart of a method for determining the overall content style characteristics of a user for comprehensive analysis in this solution;

[0065] Figure 4 A flow chart of a method for determining a standardized access index for each synchronized user access information in the present solution;

[0066] Figure 5 A flow chart of a method for determining individual content style characteristics of a user for each display terminal in this solution;

[0067] Figure 6 A flow chart of the method for building a recommended content library for each display terminal in this solution;

[0068] Figure 7 A flow chart of the method for intelligently recommending content for corresponding display terminals in this solution;

[0069] Figure 8 This is the structural block diagram of the intelligent planning system for display terminal content based on big data feedback proposed in this plan. DETAILED DESCRIPTION

[0070] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0071] Reference Figure 1As shown, a method for intelligent planning of display terminal content based on big data feedback includes:

[0072] Obtain user access information of all display terminals associated with the account;

[0073] Synchronize user access information of different display terminals associated with the account, and determine the overall content style characteristics of the user through comprehensive analysis based on the synchronized user access information;

[0074] Based on individually analyzing and processing user access information of each display terminal associated with the account, and based on the individually analyzed and processed user access information, analyzing and determining the individual content style characteristics of the user for each display terminal;

[0075] Building a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal;

[0076] Based on the recommended content library of each display terminal, intelligent content recommendation planning for the corresponding display terminal is performed.

[0077] This solution determines the user's overall content style characteristics by conducting a comprehensive analysis of all the user's access information on all types of display terminals, and uses the overall content style characteristics as the basis for content recommendation. At the same time, it analyzes the user's usage habits on different display terminals based on the user's access information on each different display terminal, and recommends content to the user on different display terminals.

[0078] Reference Figure 2 As shown, synchronizing user access information of different display terminals associated with accounts specifically includes:

[0079] Based on unique screening of user identity information in each user's access information;

[0080] Synchronize user access information of display terminals with the same user identity information to obtain an access information library corresponding to the user identity information;

[0081] Adding a timestamp to user access information based on the access time of each user access information;

[0082] Determine an information synchronization statistical cycle, and determine the synchronization processing time based on the information synchronization statistical cycle;

[0083] All user access information with a timestamp after the synchronization processing time in the access information database corresponding to the user identity information is screened out and recorded as the user access information after synchronization processing.

[0084] Among them, it is understandable that for user behavior analysis, it is only necessary to analyze the access information in a recent period of time to obtain the user's access habits. Based on this, this solution sets an information synchronization statistical period, and only synchronizes and counts the visits within the information synchronization statistical period, and eliminates historical data that is far away and has little statistical significance. In this way, the access habits that are most suitable for the user's current stage can be dynamically collected, and the user's style characteristics can be analyzed more accurately, while greatly reducing the amount of data analysis and reducing the pressure of data analysis.

[0085] Reference Figure 3 As shown, based on the synchronously processed user access information, the overall content style characteristics of the user are determined by comprehensive analysis, including:

[0086] Determine the style feature matrix A of each synchronized user access information i , A i =[a i1 … a ij …a im ], where a ij is the j-th style feature value of the user access information after the i-th synchronization processing, and m is the total number of style features;

[0087] Determine the standardized access index of each synchronized user access information;

[0088] Based on the style feature matrix of each synchronized user access information and the standardized access index of each synchronized user access information, the user's overall content style feature matrix B is constructed, B = [b1 … b j … b m ], where b j is the j-th style feature value of the user;

[0089] in:

[0090]

[0091] In the formula, ε i is the standardized access index of the user access information after the ith synchronization process, and n is the total number of user access information after the synchronization process.

[0092] Reference Figure 4 As shown, the standardized access index for determining each synchronized user access information specifically includes:

[0093] Determine an access quantization parameter based on the attributes of the user access information, wherein the access quantization parameter is determined according to the content attributes of the display terminal. For example, when the displayed content is a video, the access quantization parameter can be set to the duration of watching the video or the number of times the video is watched;

[0094] Determine a parameter value of an access quantization parameter of each synchronized user access information;

[0095] Based on the parameter value of the access quantization parameter of each synchronized user access information, a standardized access index of each synchronized user access information is calculated by a standardized processing formula;

[0096] The specific formula for standardization is:

[0097]

[0098] Where, L i is the parameter value of the access quantization parameter of the i-th user access information after synchronization processing.

[0099] Reference Figure 5 As shown, determining the individual content style characteristics of the user for each display terminal specifically includes:

[0100] Filter out a number of user access information corresponding to the display terminal from the synchronously processed user access information, and record them as individual access information;

[0101] Based on the style feature matrix of each individual access information and the standardized access index of each individual access information, the user's individual content style feature matrix C for the display terminal is constructed, C = [c1 … c j … c m ], where c j is the j-th style feature value of the user for the display terminal;

[0102] in:

[0103]

[0104] In the formula, ε x is the standardized access index of the xth individual access information, y is the total number of individual access information, a xj The j-th style feature value of the x-th individual access information.

[0105] The style feature value is set by the attribute of the style feature. For example, when the style feature is funny style, the style feature value can be set to a value between 0 and 1. The closer the value is to 1, the funnier the content is, and the closer the value is to 0, the less funny the content is. When the style feature is the length of the video, the style feature value can be set to the total length of the video.

[0106] By combining the user's access frequency and access duration for each different access information with the style feature matrix of each access information, a comprehensive analysis of the user's style tendencies under each style feature is conducted. In this way, the overall content style features representing the user's overall tendencies towards content and the individual content style features representing the user's individual tendencies towards content on different display terminals are derived.

[0107] Reference Figure 6 As shown, based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal, constructing a recommended content library for each display terminal specifically includes:

[0108] Determine the style feature matrix D of each content in the current database, D = [d1 … d j …d m ], where d j is the j-th style feature value of each content in the database;

[0109] Based on the style feature matrix of each content in the current database, the overall content style feature of the user and the individual content style feature of the user for each display terminal, the recommendation index of each content in the database for each display terminal of the user is calculated by an adaptation formula;

[0110] Filter out a number of contents whose recommendation index is less than a preset value to form a recommended content library of the display terminal;

[0111] The specific adaptation formula is:

[0112]

[0113] Where S is the recommendation index of the content to the user's display terminal, and γ is the individual style correction value of the display terminal.

[0114] Among them, under normal conditions, the value range of the individual style correction value of the display terminal is between 2-5. When the gap between the user's overall content style characteristics and the individual content style characteristics of the display terminal is greater, the individual style correction value of the display terminal will be greater, ensuring that the user can obtain content that is closer to his or her usage habits on the display terminal.

[0115] Reference Figure 7 As shown, based on the recommended content library of each display terminal, the content intelligent recommendation planning for the corresponding display terminal specifically includes:

[0116] According to the recommendation index of the content in the recommended content library of the display terminal, a priority is added to each content in the recommended content library in ascending order;

[0117] Intelligently recommend content to corresponding display terminals in order of priority.

[0118] It can be understood that the recommendation index represents the style difference distance between the content and the user's overall content style characteristics and the user's individual content style characteristics for each display terminal. The smaller the value, the smaller the style difference distance between the content and the overall content style characteristics and the user's individual content style characteristics for each display terminal, and the more suitable the content is for the user's style. Based on this, this solution adds priority to each content in the recommended content library in order from small to large, and intelligently recommends content for the corresponding display terminal in order of priority, so that users can obtain the most suitable content recommendations in turn.

[0119] Further, see Figure 8 As shown, based on the same inventive concept as above, this solution proposes a display terminal content intelligent planning system based on big data feedback, including:

[0120] An information collection module, which is used to obtain user access information of all display terminals associated with an account;

[0121] An information preprocessing module, which is electrically connected to the information collection module, and is used to synchronously process all user access information collected by the information collection module to obtain the synchronously processed user access information, and at the same time classify all user access information collected by the information collection module according to different display terminals;

[0122] An overall style analysis module, the overall style analysis module is electrically connected to the information preprocessing module, and the overall style analysis module is used to comprehensively analyze and determine the overall content style characteristics of the user based on the synchronously processed user access information;

[0123] An individual style analysis module, the individual style analysis module is electrically connected to the information preprocessing module, and the individual style analysis module is used to analyze and determine the individual content style characteristics of the user for each display terminal based on the user access information after individual analysis and processing;

[0124] A comprehensive planning module, which is electrically connected to the overall style analysis module and the individual style analysis module. The comprehensive planning module is used to build a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal, and to perform intelligent content recommendation planning for the corresponding display terminal based on the recommended content library for each display terminal.

[0125] The overall style analysis module includes:

[0126] An information processing unit, the information processing unit is used to determine a style feature matrix of each synchronously processed user access information and determine a standardized access index of each synchronously processed user access information;

[0127] The overall style fitting unit is used to construct the user's overall content style feature matrix based on the style feature matrix of each synchronized user access information and the standardized access index of each synchronized user access information.

[0128] The individual style analysis module includes:

[0129] An information screening unit, the information screening unit is electrically connected to the information processing unit, and is used to screen out a number of user access information corresponding to the display terminal, recorded as individual access information, and obtain a style feature matrix of the individual access information and a standardized access index of the individual access information from the information processing unit;

[0130] The individual style fitting unit is used to construct an individual content style feature matrix of the user for the display terminal based on the style feature matrix of each individual access information and the standardized access index of each individual access information.

[0131] The usage process of the above-mentioned display terminal content intelligent planning system based on big data feedback is as follows:

[0132] Step 1: The information collection module obtains user access information of all display terminals associated with the account;

[0133] Step 2: The information preprocessing module synchronously processes all the user access information collected by the information collection module to obtain the synchronously processed user access information, and at the same time classifies all the user access information collected by the information collection module according to different display terminals to obtain the user access information after separate analysis and processing for each display terminal;

[0134] Step 3: The overall style analysis module calls the information processing unit to determine the style feature matrix of each synchronized user access information and the standardized access index of each synchronized user access information, and then calls the overall style fitting unit to construct the user's overall content style feature matrix based on the style feature matrix of each synchronized user access information and the standardized access index of each synchronized user access information;

[0135] Step 4: The individual style analysis module calls the information screening unit to screen out a number of user access information corresponding to the display terminal, recorded as individual access information, and obtains the style feature matrix of the individual access information and the standardized access index of the individual access information from the information processing unit, and constructs the user's individual content style feature matrix for the display terminal based on the style feature matrix of each individual access information and the standardized access index of each individual access information through the individual style fitting unit;

[0136] Step 5: The comprehensive planning module builds a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal, and performs intelligent content recommendation planning for the corresponding display terminal based on the recommended content library for each display terminal.

[0137] In summary, the advantages of the present invention are: effectively ensuring that the user receives the content of interest while achieving the compatibility of the recommended content with the display terminal, thereby greatly improving the user experience.

[0138] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for intelligent planning of display terminal content based on big data feedback, characterized in that: include: Obtain user access information of all display terminals associated with the account; Synchronize user access information of different display terminals associated with the account, and determine the overall content style characteristics of the user through comprehensive analysis based on the synchronized user access information; Based on individually analyzing and processing user access information of each display terminal associated with the account, and based on the individually analyzed and processed user access information, analyzing and determining the individual content style characteristics of the user for each display terminal; Building a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal; Based on the recommended content library of each display terminal, intelligent content recommendation planning is performed for the corresponding display terminal; The comprehensive analysis based on the synchronously processed user access information to determine the user's overall content style characteristics specifically includes: Determine the style feature matrix A of each synchronized user access information i , A i =[a i1 … a ij … a im ], where a ij is the j-th style feature value of the user access information after the i-th synchronization processing, and m is the total number of style features; Determine the standardized access index of each synchronized user access information; Based on the style feature matrix of each synchronized user access information and the standardized access index of each synchronized user access information, the user's overall content style feature matrix B is constructed, B = [b1 … b j … b m ], where b j is the j-th style feature value of the user; in: In the formula, ε i is the standardized access index of the user access information after the ith synchronization process, and n is the total number of user access information after the synchronization process; Determining the standardized access index of each synchronized user access information specifically includes: Determine an access quantification parameter based on the attribute of the user access information, where the access quantification parameter is any one of access duration and access times; Determine a parameter value of an access quantization parameter of each synchronized user access information; Based on the parameter value of the access quantization parameter of each synchronized user access information, a standardized access index of each synchronized user access information is calculated by a standardized processing formula; The standardization processing formula is specifically: Where, L i is the parameter value of the access quantization parameter of the i-th user access information after synchronization processing; Determining the individual content style characteristics of each display terminal of the user specifically includes: Filter out a number of user access information corresponding to the display terminal from the synchronously processed user access information, and record them as individual access information; Based on the style feature matrix of each individual access information and the standardized access index of each individual access information, the user's individual content style feature matrix C for the display terminal is constructed, C = [c1 … c j … c m ], where c j is the j-th style feature value of the user for the display terminal; in: In the formula, ε x is the standardized access index of the xth individual access information, y is the total number of individual access information, a xj The j-th style feature value of the x-th individual access information; The step of constructing a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal specifically includes: Determine the style feature matrix D of each content in the current database, D = [d1 … d j …d m ], where d j is the j-th style feature value of each content in the database; Based on the style feature matrix of each content in the current database, the overall content style feature of the user and the individual content style feature of the user for each display terminal, the recommendation index of each content in the database for each display terminal of the user is calculated by an adaptation formula; Filter out a number of contents whose recommendation index is less than a preset value to form a recommended content library of the display terminal; The adaptation formula is specifically: Where S is the recommendation index of the content to the user's display terminal, and γ is the individual style correction value of the display terminal.

2. The method for intelligent planning of display terminal content based on big data feedback according to claim 1, characterized in that: The synchronous processing of user access information of different display terminals associated with accounts specifically includes: Based on unique screening of user identity information in each user's access information; Synchronize user access information of display terminals with the same user identity information to obtain an access information library corresponding to the user identity information; Adding a timestamp to user access information based on the access time of each user access information; Determine an information synchronization statistical cycle, and determine the synchronization processing time based on the information synchronization statistical cycle; All user access information with a timestamp after the synchronization processing time in the access information database corresponding to the user identity information is screened out and recorded as the user access information after synchronization processing.

3. The method for intelligent planning of display terminal content based on big data feedback according to claim 2 is characterized in that: The intelligent content recommendation planning for the corresponding display terminal based on the recommended content library of each display terminal specifically includes: According to the recommendation index of the content in the recommended content library of the display terminal, a priority is added to each content in the recommended content library in ascending order; Intelligently recommend content to corresponding display terminals in order of priority.

4. A display terminal content intelligent planning system based on big data feedback, characterized in that: A method for intelligently planning display terminal content based on big data feedback according to any one of claims 1 to 3, comprising: An information collection module, the information collection module is used to obtain user access information of all display terminals associated with the account; An information preprocessing module, the information preprocessing module is electrically connected to the information acquisition module, and the information preprocessing module is used to synchronously process all user access information collected by the information acquisition module to obtain the synchronously processed user access information, and at the same time classify all user access information collected by the information acquisition module according to different display terminals; An overall style analysis module, the overall style analysis module is electrically connected to the information preprocessing module, and the overall style analysis module is used to comprehensively analyze and determine the overall content style characteristics of the user based on the synchronously processed user access information; An individual style analysis module, the individual style analysis module is electrically connected to the information preprocessing module, and the individual style analysis module is used to analyze and determine the individual content style characteristics of the user for each display terminal based on the user access information that has been individually analyzed and processed; A comprehensive planning module, the comprehensive planning module is electrically connected to the overall style analysis module and the individual style analysis module, and is used to construct a recommended content library for each display terminal based on the user's overall content style characteristics and the user's individual content style characteristics for each display terminal, and based on the recommended content library for each display terminal, perform intelligent content recommendation planning for the corresponding display terminal.

5. The display terminal content intelligent planning system based on big data feedback according to claim 4 is characterized in that: The overall style analysis module includes: An information processing unit, the information processing unit is used to determine a style feature matrix of each synchronously processed user access information and determine a standardized access index of each synchronously processed user access information; The overall style fitting unit is used to construct the overall content style feature matrix of the user based on the style feature matrix of each synchronously processed user access information and the standardized access index of each synchronously processed user access information.

6. The display terminal content intelligent planning system based on big data feedback according to claim 5 is characterized in that: The individual style analysis module includes: An information screening unit, the information screening unit being electrically connected to the information processing unit, and configured to screen out a plurality of user access information corresponding to the display terminal, recorded as individual access information, and obtain a style feature matrix of the individual access information and a standardized access index of the individual access information from the information processing unit; The individual style fitting unit is used to construct an individual content style feature matrix of the user for the display terminal based on the style feature matrix of each individual access information and the standardized access index of each individual access information.

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