Data analysis method and device and electronic equipment
By generating directed graphs, especially Sanki graphs, and fully displaying user behavior paths, the problem that Sanki graphs cannot fully display long behavior paths are solved, and the accuracy and efficiency of data analysis are improved.
Patent Information
- Application Number
- CN202510437940.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-08
- Publication Date
- 2025-08-19
AI Technical Summary
In the prior art, Sanki maps cannot fully display the long behavioral paths in the user behavior path, resulting in inaccurate data analysis results.
By obtaining the target user's behavior data link in the target application, the front path of each behavior path is determined, and a directed graph, especially a Sankey graph, is generated based on the candidate behavior path, to show the user's behavior.
It realizes the complete display of long behavioral paths for users and improves the accuracy and efficiency of data analysis.
Smart Images
Figure CN120508682A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data analysis technology, and in particular, to a data analysis method, device, and electronic device. Background Art
[0002] User behavior analysis is the process of collecting and analyzing traffic data from users accessing related applications, thereby uncovering patterns in user access. Related technologies can analyze user traffic data using directed graphs (such as Sankey diagrams).
[0003] Since the corresponding behavior paths of traffic data include short behavior paths and long behavior paths, the Sankey diagram cannot fully display the corresponding data situation for the long behavior path, which can easily lead to inaccurate data analysis results. Summary of the Invention
[0004] The purpose of the present disclosure is to provide a data analysis method, device and electronic equipment.
[0005] In order to achieve the above objectives, in a first aspect, the present disclosure provides a data analysis method, the method comprising: Acquire a behavior data chain of a target user in a target application, wherein the behavior data chain includes multiple behavior paths of the target user in the target application; For each behavior path, determine the previous path included in the behavior path; the previous path is the previous path from the starting point to the end node of the behavior path; determining a candidate behavior path of the behavior data chain according to the plurality of behavior paths and the preceding paths of the plurality of behavior paths; Based on the candidate behavior path, a directed graph corresponding to the behavior data chain is generated, so as to display the target user behavior corresponding to the behavior data chain through the directed graph.
[0006] Optionally, determining the candidate behavior paths of the behavior data chain according to the multiple behavior paths and the previous paths of the multiple behavior paths includes: For each behavior path, determine one or more first path nodes of the behavior path and one or more second path nodes of the preceding path using a pre-stored path storage table; The candidate behavior path is obtained based on a difference between the first path node and the second path node.
[0007] Optionally, the method further includes: Acquire first query information input by the target user; the first query information includes a path node; determining a target behavior path from the directed graph according to the first query information; The target behavior path corresponding to the first query information is displayed.
[0008] Optionally, the method further includes: Acquire second query information input by the target user; the second query information includes path nodes and access times; Based on the path nodes, the number of visits and the directed graph, a target graph is generated to display the target user behavior corresponding to the second query information through the target graph.
[0009] Optionally, the directed graph includes a Sankey diagram.
[0010] In a second aspect, the present disclosure provides a data analysis device, comprising: An acquisition module, configured to acquire a behavior data chain of a target user in a target application, wherein the behavior data chain includes multiple behavior paths of the target user in the target application; A first determining module is configured to determine, for each behavior path, a previous path included in the behavior path; the previous path is a previous path from the starting point to the end node of the behavior path; a second determining module, configured to determine a candidate behavior path of the behavior data chain according to the plurality of behavior paths and the preceding paths of the plurality of behavior paths; A generating module is used to generate a directed graph corresponding to the behavior data chain based on the candidate behavior path, so as to display the target user behavior corresponding to the behavior data chain through the directed graph.
[0011] Optionally, the second determining module is configured to determine, for each behavior path, one or more first path nodes of the behavior path and one or more second path nodes of the preceding path using a pre-stored path storage value table; The candidate behavior path is obtained based on a difference between the first path node and the second path node.
[0012] Optionally, the device further comprises: A first acquisition module is configured to acquire first query information input by the target user; the first query information includes a path node; a third determining module, configured to determine a target behavior path from the directed graph according to the first query information; The first display module is used to display the target behavior path corresponding to the first query information.
[0013] Optionally, the device further comprises: A second acquisition module is configured to acquire second query information input by the target user; the second query information includes a path node and a number of visits; The second display module is used to generate a target graph based on the path nodes, the number of visits and the directed graph, so as to display the target user behavior corresponding to the second query information through the target graph.
[0014] Optionally, the directed graph includes a Sankey diagram.
[0015] In a third aspect, the present disclosure provides an electronic device, comprising: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the data analysis method described in the first aspect above.
[0016] Through the above technical solution, a corresponding directed graph can be generated based on the behavior data chain, thereby fully displaying the user behavior path in the behavior data chain, realizing the analysis of the user's long behavior path, and thus improving the accuracy of data analysis.
[0017] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 The figure is a flow chart showing a data analysis method according to an exemplary embodiment.
[0019] Figure 2 is based on Figure 1 A flow chart of a data analysis method is shown in accordance with an exemplary embodiment.
[0020] Figure 3 The figure is a schematic diagram showing the effect of a Sankey diagram according to an exemplary embodiment.
[0021] Figure 4 is based on Figure 2 A flow chart of a data analysis method is shown in accordance with an exemplary embodiment.
[0022] Figure 5 is based on Figure 4 A flow chart of a data analysis method is shown in accordance with an exemplary embodiment.
[0023] Figure 6 The figure is a schematic diagram showing the effect of a funnel chart according to an exemplary embodiment.
[0024] Figure 7 The figure is a flowchart of another data analysis method according to an exemplary embodiment.
[0025] Figure 8 It is a block diagram of a data analysis device according to an exemplary embodiment.
[0026] Figure 9 is based on Figure 8 A block diagram of a data analysis device is shown in accordance with an exemplary embodiment of the present invention.
[0027] Figure 10 is based on Figure 9 A block diagram of a data analysis device is shown in accordance with an exemplary embodiment of the present invention.
[0028] Figure 11 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION
[0029] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.
[0030] First, let's introduce the application scenarios of this application. This application is used in data analysis scenarios. For example, it can analyze the user access data of a certain application. User behavior analysis refers to the process of collecting statistics and analyzing the traffic data of users accessing related applications, from which we can discover the relevant patterns of users accessing related applications.
[0031] In related technologies, directed graphs (such as Sankey diagrams) can be used to analyze user traffic data. Because traffic data corresponds to short and long behavior paths, Sankey diagrams cannot fully display the corresponding data for long behavior paths, which can easily lead to inaccurate data analysis results.
[0032] For example, a user's teaching behavior in an education-related app can be a long path of action, encompassing multiple steps, such as learning, studying, and taking exams. Each step is complex. Therefore, the traffic data corresponding to each step is crucial. Abnormal changes in traffic data (such as sudden increases or decreases, data loss, etc.) can make it difficult to clearly identify the user's behavior.
[0033] In order to solve the above problems, the present disclosure provides a data analysis method, device and electronic device; obtain a behavior data chain of a target user in a target application, and the behavior data chain includes multiple behavior paths of the target user in the target application; for each behavior path, determine the previous path contained in the behavior path; the previous path is the previous path of the path from the starting point to the end node of the behavior path; according to the multiple behavior paths and the previous paths of the multiple behavior paths, determine the candidate behavior path of the behavior data chain; based on the candidate behavior path, generate a directed graph corresponding to the behavior data chain, so as to display the user behavior corresponding to the behavior data chain through the directed graph; through the above technical solution, a corresponding directed graph can be generated based on the behavior data chain, thereby fully displaying the user behavior path in the behavior data chain, realizing the analysis of the user's long behavior path, and thereby improving the accuracy of data analysis.
[0034] Figure 1 FIG. 1 is a flow chart showing a data analysis method according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps.
[0035] S101. Obtain a target user's behavior data chain in a target application.
[0036] The behavior data chain may include multiple behavior paths of the target user in the target application.
[0037] For example, the behavior data chain can be data pre-stored in a server. The target application can be a PC application, a mobile application, a network service platform, etc., which are not limited here. The behavior path can include a start identifier and an end identifier; the behavior path can include multiple nodes, such as a starting point, an intermediate node, and an end node. In some embodiments, the start identifier can be a starting point, and the end identifier can be a node.
[0038] S102: For each behavior path, determine the previous path included in the behavior path.
[0039] The previous path may be the previous path from the starting point to the end node of the behavior path.
[0040] For example, the behavior path may include nodes a, b, c, and d, and the previous path of the behavior path may include nodes a, b, and c.
[0041] S103: Determine candidate behavior paths for the behavior data chain based on the multiple behavior paths and the previous paths of the multiple behavior paths.
[0042] S104: Based on the candidate behavior path, generate a directed graph corresponding to the behavior data chain, so as to display the user behavior corresponding to the behavior data chain through the directed graph.
[0043] For example, the directed graph may include a Sankey diagram.
[0044] In some embodiments, the above S104 may include: generating a directed graph corresponding to the behavior data chain based on the candidate behavior path by using pre-set image generation software.
[0045] For example, the pre-set image generation software can be an existing image generation software, which will not be described in detail here. In this way, the data analysis method can generate a directed graph using the existing image generation software, thereby improving the flexibility of image drawing, making the method more compatible, and thus improving work efficiency.
[0046] Through the above technical solution, a corresponding directed graph can be generated based on the behavior data chain, thereby fully displaying the user behavior path in the behavior data chain, realizing the analysis of the user's long behavior path, and thus improving the accuracy of data analysis.
[0047] Figure 2 is based on Figure 1 Flowchart of a data analysis method shown in FIG. Figure 2 As shown, the above S103 may include: S1031. For each behavior path, determine one or more first path nodes of the behavior path and one or more second path nodes of the previous path using a pre-stored path value table.
[0048] For example, the path value storage table may be pre-stored in the server. The path value storage table may include a behavior path and a preceding path.
[0049] For example, the path value table can be as shown in Table 1 below, including start_vertex, which represents the starting point of the path; path, which represents the behavior path; pre_path, which represents the previous path; step, which represents the number of steps; and ev, which represents the number of visits. S is the start identifier, and bg is the end identifier. Taking the behavior path in row 4 as an example, the first path nodes of this behavior path are s, a, b, and c, and the corresponding previous path of this behavior path includes the second path nodes a and b.
[0050] Table 1:
[0051] It should be noted that the above-mentioned number of steps may refer to the number of times the target user jumps between different levels in the target application. The target application may include multiple levels. When the user uses the target application, they will jump between different levels, thereby generating corresponding traffic data. In related technologies, a corresponding behavior data chain can be generated based on the traffic data generated by the user and stored in the corresponding server. Since the process of generating the behavior data chain is existing technology, it will not be repeated here.
[0052] S1032: Obtain the candidate behavior path based on the difference between the first path node and the second path node.
[0053] For example, taking the behavior path of the row with sequence number 5 as an example, the behavior path of this row includes the first path nodes s, a, b, c, d, and the corresponding second path nodes of the previous path are a, b, c. At this time, the difference between the first path node and the second path node can get s, d, so s, d are the candidate behavior paths corresponding to this row.
[0054] For example, the candidate behavior paths may include multiple paths; after determining the pending behavior paths corresponding to each behavior path, the sum of the multiple pending paths can be used as the candidate behavior path. In this way, the corresponding candidate behavior paths can be determined based on the data in the path storage table, thereby improving work efficiency.
[0055] It should be noted that when each starting point steps 0 steps, there is no previous path, and each path including the tail identifier uses the full path as the previous path. For example, with "s", "a", "b", etc. as the starting point, the path that steps 0 steps has no previous path. Taking the path of the row where serial number 7 is located as an example, the behavior path includes "s,a,b,c,d,e,bg", and the corresponding previous path is "s,a,b,c,d,e". Taking the path of the row where serial number 13 is located as an example, the behavior path includes "s,b,e,f,bg" and the corresponding previous path is "s,b,e,f".
[0056] For example, if Figure 3 As shown, Figure 3 This is a schematic diagram of the effect of a Sankey diagram generated based on the above-mentioned candidate behavior paths. It can be seen from the figure that the target application can include multiple levels, and the user has a complete behavior path between multiple levels, that is, including complete out-degree and in-degree. Among them, in-degree usually refers to the sum of the number of times a node in a directed graph serves as the end point of the directed graph, and out-degree usually refers to the sum of the number of times a node in a directed graph serves as the starting point of the directed graph. Therefore, each level in the directed graph generated by this method includes a complete behavior path, which can intuitively display the complete user behavior, making it easier for technical personnel to analyze user behavior based on the Sankey diagram analysis.
[0057] It should be noted that the above Figure 3 It is just to show the visual effect of the generated Sankey diagram to help readers understand the style of the Sankey diagram.
[0058] Figure 4 is based on Figure 2 Flowchart of a data analysis method shown in FIG. Figure 4 As shown, the method may further include the following steps.
[0059] S105: Acquire first query information input by the target user.
[0060] The first query information may include a path node.
[0061] For example, first query information input by the target user through an interactive interface may be obtained; the interactive interface may be an HMI. The path node may be one or more of a starting point, an intermediate node, or an end node of the behavior path.
[0062] S106: Determine a target behavior path from the directed graph according to the first query information.
[0063] For example, the target behavior path may be one or more.
[0064] S107: Display the target behavior path corresponding to the first query information.
[0065] For example, the target behavior path can be formatted in a preset format to display the target behavior path in the directed graph; or the target behavior path can be displayed in a new directed graph using the preset format. The format adjustment method may include adjusting one or more of color, line, and, which are not limited herein.
[0066] For example, the color of the target behavior path may be adjusted to distinguish the target behavior path from other behavior paths in the directed graph; the other behavior paths may be behavior paths in the directed graph other than the target behavior path.
[0067] Figure 5 is based on Figure 4 Flowchart of a data analysis method shown in FIG. Figure 5 As shown, the method may further include the following steps.
[0068] S108: Acquire second query information input by the target user.
[0069] The second query information may include path nodes and access times.
[0070] For example, the second query information may include multiple query parameters, including the path node and a visit count. The visit count may refer to the number of times the distance node has been viewed by a user. The path node and visit count may be string information, and the multiple query parameters may be separated by symbols. For example, the second query information may be "path node% visit count."
[0071] S109: Generate a target graph based on the path node, the number of visits, and the directed graph, so as to display the target user behavior corresponding to the second query information through the target graph.
[0072] For example, the target graph can be a funnel graph; or the target graph can be a statistical chart. The form of the statistical chart is not limited and can be a bar chart or a pie chart. In this way, the user's access to a specific path node can be displayed separately, which can filter a large amount of data, making it easier for users to view relevant data and further facilitate users to analyze the relevant data, thereby improving the efficiency of data analysis.
[0073] For example, if Figure 6 The figure below shows a funnel chart effect diagram, which includes multiple nodes and the query results of each node. In this way, you can further query related information through multiple query parameters based on the directed graph, filter relevant data, and facilitate data analysis for users.
[0074] It should be noted that the above Figure 6 The purpose is just to show the visual effect of the generated funnel chart. The parameters shown in the figure are just examples and are not limiting.
[0075] Figure 7 FIG. 1 is a flow chart of another data analysis method according to an exemplary embodiment. Figure 7 As shown, the method may include the following steps.
[0076] S701: Obtain a target user's behavior data chain in a target application.
[0077] The behavior data chain may include multiple behavior paths of the target user in the target application.
[0078] S702: For each behavior path, determine the previous path included in the behavior path.
[0079] S703: For each behavior path, determine one or more first path nodes of the behavior path and one or more second path nodes of the previous path using a pre-stored path value table.
[0080] S704: Obtain the candidate behavior path based on the difference between the first path node and the second path node.
[0081] S705: Generate a directed graph corresponding to the behavior data chain based on the candidate behavior path, so as to display the user behavior corresponding to the behavior data chain through the directed graph.
[0082] S706: Acquire first query information input by the target user.
[0083] The first query information includes a path node.
[0084] S707: Determine a target behavior path from the directed graph according to the first query information.
[0085] S708: Display the target behavior path corresponding to the first query information.
[0086] S709: Acquire second query information input by the target user.
[0087] The second query information may include path nodes and access times.
[0088] S710: Generate a target graph based on the path node, the number of visits, and the directed graph, so as to display the target user behavior corresponding to the second query information through the target graph.
[0089] Through the above technical solution, a corresponding directed graph can be generated based on the behavior data chain, thereby fully displaying the user behavior path in the behavior data chain, realizing the analysis of the user's long behavior path, and thus improving the accuracy of data analysis.
[0090] Figure 8 FIG. 1 is a block diagram of a data analysis device according to an exemplary embodiment. Figure 6 As shown, the apparatus 800 may include an acquisition module 810, a first determination module 820, a second determination module 830, and a generation module 840; The acquisition module 810 is used to acquire a behavior data chain of a target user in a target application, where the behavior data chain includes multiple behavior paths of the target user in the target application; The first determining module 820 is configured to determine, for each behavior path, a preceding path included in the behavior path; the preceding path being the previous path from the starting point to the end node of the behavior path; The second determining module 830 is configured to determine a candidate behavior path of the behavior data chain according to the plurality of behavior paths and the preceding paths of the plurality of behavior paths; The generating module 840 is used to generate a directed graph corresponding to the behavior data chain based on the candidate behavior path, so as to display the target user behavior corresponding to the behavior data chain through the directed graph.
[0091] Through the above technical solution, a corresponding directed graph can be generated based on the behavior data chain, thereby fully displaying the user behavior path in the behavior data chain, realizing the analysis of the user's long behavior path, and thus improving the accuracy of data analysis.
[0092] Optionally, the second determination module 830 is used to determine, for each behavior path, one or more first path nodes of the behavior path and one or more second path nodes of the preceding path through a pre-stored path value table; and obtain the candidate behavior path based on the difference between the first path node and the second path node.
[0093] Figure 9 is based on Figure 9 FIG. 1 is a block diagram of a data analysis device shown in FIG. Figure 9 As shown, the apparatus 800 may further include: a first acquisition module 850, a third determination module 860 and a first display module 870; The first acquisition module 850 is used to acquire first query information input by the target user; the first query information includes a path node; The third determining module 860 is configured to determine a target behavior path from the directed graph according to the first query information; The first display module 870 is used to display the target behavior path corresponding to the first query information.
[0094] Figure 10 is based on Figure 9 FIG. 1 is a block diagram of a data analysis device shown in FIG. Figure 10 As shown, the device 800 may further include: a second acquisition module 880 and a second display module 890 .
[0095] The second acquisition module 880 is used to acquire the second query information input by the target user; the second query information includes the path node and the number of visits; The second display module 890 is used to generate a target graph based on the path node, the number of visits and the directed graph, so as to display the target user behavior corresponding to the second query information through the target graph.
[0096] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.
[0097] In summary, the present disclosure provides a data analysis method, device and electronic device; obtaining a behavior data chain of a target user in a target application, the behavior data chain including multiple behavior paths of the target user in the target application; for each behavior path, determining a previous path included in the behavior path; the previous path is the previous path of the path from the starting point to the end node of the behavior path; determining a candidate behavior path of the behavior data chain based on the multiple behavior paths and the previous paths of the multiple behavior paths; generating a directed graph corresponding to the behavior data chain based on the candidate behavior path, so as to display the user behavior corresponding to the behavior data chain through the directed graph; through the above technical solution, a corresponding directed graph can be generated based on the behavior data chain, thereby fully displaying the user behavior path in the behavior data chain, realizing analysis of the user's long behavior path, and thereby improving the accuracy of data analysis.
[0098] Figure 11 1 is a block diagram of an electronic device 1100 according to an exemplary embodiment. For example, the electronic device 1100 may be provided as a server. Figure 11 The electronic device 1100 includes a processor 1122, which may be one or more, and a memory 1132 for storing a computer program executable by the processor 1122. The computer program stored in the memory 1132 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 1122 may be configured to execute the computer program to perform the above-mentioned data analysis method.
[0099] In addition, the electronic device 1100 may further include a power supply component 1126 and a communication component 1150. The power supply component 1126 may be configured to perform power management of the electronic device 1100, and the communication component 1150 may be configured to implement communication of the electronic device 1100, for example, wired or wireless communication. In addition, the electronic device 1100 may further include an input / output interface 1158. The electronic device 1100 may operate based on an operating system stored in the memory 1132, such as Windows Server 2003. TM , Mac OS X TM , Unix TM , Linux TM etc.
[0100] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the above-described data analysis method. For example, the computer-readable storage medium may be the aforementioned memory 1132 including the program instructions. The program instructions may be executed by the processor 1122 of the electronic device 1100 to perform the above-described data analysis method.
[0101] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program that can be executed by a processor. When the computer program is executed by the processor, the steps of the above-mentioned data analysis method are implemented.
[0102] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.
[0103] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.
[0104] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.
Claims
1. A data analysis method, characterized in that: The method comprises: Acquire a behavior data chain of a target user in a target application, wherein the behavior data chain includes multiple behavior paths of the target user in the target application; For each behavior path, determine the previous path included in the behavior path; the previous path is the previous path from the starting point to the end node of the behavior path; determining a candidate behavior path of the behavior data chain according to the plurality of behavior paths and the preceding paths of the plurality of behavior paths; Based on the candidate behavior path, a directed graph corresponding to the behavior data chain is generated, so as to display the target user behavior corresponding to the behavior data chain through the directed graph.
2. The method according to claim 1, characterized in that Determining the candidate behavior paths of the behavior data chain according to the multiple behavior paths and the previous paths of the multiple behavior paths includes: For each behavior path, determine one or more first path nodes of the behavior path and one or more second path nodes of the preceding path using a pre-stored path storage table; The candidate behavior path is obtained based on a difference between the first path node and the second path node.
3. The method according to claim 1, characterized in that The method further comprises: Acquire first query information input by the target user; the first query information includes a path node; determining a target behavior path from the directed graph according to the first query information; The target behavior path corresponding to the first query information is displayed.
4. The method according to claim 1, wherein The method further comprises: Acquire second query information input by the target user; the second query information includes path nodes and access times; Based on the path nodes, the number of visits and the directed graph, a target graph is generated to display the target user behavior corresponding to the second query information through the target graph.
5. The method according to any one of claims 1 to 4, characterized in that The directed graph includes a Sankey diagram.
6. A data analysis device, characterized in that: The device comprises: An acquisition module, configured to acquire a behavior data chain of a target user in a target application, wherein the behavior data chain includes multiple behavior paths of the target user in the target application; A first determining module is configured to determine, for each behavior path, a previous path included in the behavior path; the previous path is a previous path from the starting point to the end node of the behavior path; a second determining module, configured to determine a candidate behavior path of the behavior data chain according to the plurality of behavior paths and the preceding paths of the plurality of behavior paths; A generating module is used to generate a directed graph corresponding to the behavior data chain based on the candidate behavior path, so as to display the target user behavior corresponding to the behavior data chain through the directed graph.
7. The device according to claim 6, characterized in that The second determining module is configured to determine, for each behavior path, one or more first path nodes of the behavior path and one or more second path nodes of the preceding path using a pre-stored path storage value table; The candidate behavior path is obtained based on a difference between the first path node and the second path node.
8. The device according to claim 6, characterized in that The device further comprises: A first acquisition module is configured to acquire first query information input by the target user; the first query information includes a path node; a third determining module, configured to determine a target behavior path from the directed graph according to the first query information; The first display module is used to display the target behavior path corresponding to the first query information.
9. The device according to claim 6, characterized in that The device further comprises: A second acquisition module is configured to acquire second query information input by the target user; the second query information includes a path node and a number of visits; The second display module is used to generate a target graph based on the path nodes, the number of visits and the directed graph, so as to display the target user behavior corresponding to the second query information through the target graph.
10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 5.