A big data-based user behavior analysis and fine operation method

Abstract

This invention relates to the field of behavioral analysis technology, specifically a user behavior analysis and refined operation method based on big data, comprising the following steps: obtaining jump order, access node, and time information; constructing a user path behavior chain structure; identifying similar path segments and filtering inertial feature path segment sequences; extracting and locating content nodes to form a content node path structure; matching node content to construct a behavioral chain operation content combination; setting operation points based on path features; and obtaining an operation execution plan. In this invention, by constructing a path behavior chain including jump order and dwell nodes, extracting inertial path sequences with consistent rhythm and coherence, and combining path numbers with content node hierarchy and distribution, establishing a combination relationship between paths and content, summarizing node tags and path aggregation features, and setting operation points based on frequency and density, this promotes the linkage between behavioral trajectories, content structures, and path execution, enhancing the correspondence between operational strategies and user paths.

Description

Technical Field

[0001] This invention relates to the field of behavioral analysis technology, and in particular to a method for user behavior analysis and refined operation based on big data. Background Technology

[0002] The field of behavioral analytics primarily involves the collection and analysis of behavioral data generated by individuals in specific environments. Core aspects include user behavior feature identification, behavior path modeling, behavior preference inference, and the construction of behavior-driven mechanisms. It typically employs methods such as large-scale data acquisition, multi-dimensional feature construction, behavior pattern mining, and trend prediction to systematically analyze and manage user behavior, providing foundational data support and behavioral basis for subsequent strategy formulation and operational decisions. This field emphasizes the combination of data-driven approaches and behavioral logic, widely integrating statistical modeling, cluster analysis, sequence pattern recognition, and other technical paths to form a technical system combining data analysis and behavioral research. Traditional user behavior analysis and refined operation methods refer to extracting user behavior features based on historical data such as click records, access frequency, and page dwell time generated on the platform. This involves using user tagging rules, feature frequency statistics, or behavior path summarization to classify users into several groups according to manually set segmentation conditions or empirical indicators, and then executing refined operational operations through fixed strategy content, scheduled push mechanisms, or manually configured schemes.

[0003] Existing technologies build tags and path patterns based on users' historical access records, but lack the linkage analysis of path rhythm, content hierarchy, and the integrity of behavioral sequences. When path behavior is highly dynamic or node content changes frequently, static rules and empirical conditions are difficult to effectively characterize the behavioral process structure. This results in the fact that the clustering strategy cannot reflect the continuity of user operations and the inherent relationship between nodes, and the behavioral characteristics are fragmented. This can easily lead to unreasonable setting of operational intervention nodes and the timing of strategy action deviating from the user's active cycle, affecting the efficiency of content reach. For example, in user paths with multiple page jumps but obvious path preferences, the rule clustering ignores the jump rhythm and content coherence, causing the strategy content to be disconnected, resulting in a decrease in response rate and conversion level. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for user behavior analysis and refined operation based on big data;

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a user behavior analysis and refined operation method based on big data, comprising the following steps:

[0006] S1: Obtain the jump order, access node and path time information in the user operation, connect the jump and stay nodes in sequence, bind the time field and node structure number to obtain the user path behavior chain structure;

[0007] S2: Based on the user path behavior chain structure, identify similar jump path segments, analyze the jump rhythm, the continuity of the dwell order and the correlation between the content, filter path segments with consistent behavior patterns according to the path order, and obtain the inertial feature path segment sequence.

[0008] S3: Based on the path segment number of the inertial feature path segment sequence, extract the corresponding content nodes, pair the nodes with the path segments according to the number, and locate the node position in the path segment to obtain the content node path structure.

[0009] S4: Based on the content node path structure, according to node type, tag and interaction level, identify the content of nodes with the same structure, and obtain the combination of behavioral chain operation content by corresponding content nodes and paths in the order of path segments.

[0010] S5: Based on the combination of operational content in the behavioral chain, analyze the relationship between differentiated user groups and operational tasks according to path type, access frequency and node level information, set path operation points with reference to behavioral characteristics, and obtain an operational execution plan.

[0011] As a further aspect of the present invention, the user path behavior chain structure includes a page jump structure, node access information, continuous behavior segments, time sequence field, and node structure number; the inertial feature path segment sequence includes path segments of the same jump form, path jump rhythm, dwell sequence continuity, jump content relevance, and path segment number; the content node path structure includes path segment number, content node, node path position, and path sequence identifier; the behavior chain operation content combination includes node content field, content type, content tag, interaction level, and content path pairing relationship; and the operation execution plan includes user group type, corresponding operation task, path operation point, and behavior feature tag.

[0012] As a further aspect of the present invention, the dwell node refers to the content page or interface node that a user visits and stays at a single moment in the jump path;

[0013] The continuity of the dwell sequence refers to whether the time order in which users visit and stay at nodes is logically continuous.

[0014] As a further aspect of the present invention, the interaction level refers to the display level of node content in the overall structure, and measures the level at which user behavior occurs in the operation process;

[0015] The behavioral characteristics refer to the user's behavior patterns in the path, including access frequency, jump rhythm, dwell time, and interaction methods.

[0016] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0017] S101: Obtain the page jump order, content access node order, and path time sequence tag; aggregate jump nodes and dwell nodes according to time tag; verify the order of the aggregated node sequence; and obtain the node sequence structure frame.

[0018] S102: Based on the node sequence structure frame, according to the page jump structure and node access information in the sequence, the time sequence field and structure number of the corresponding nodes are compared, and the number mapping segment relationship after comparison is divided to obtain the node segment mapping matrix.

[0019] S103: Based on the node segment mapping matrix, perform sequence connectivity judgment on the combination state of jump nodes and stay nodes in the matrix, connect the sequence set path links in the connected state according to the jump order, and obtain the user path behavior chain structure.

[0020] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0021] S201: Based on the jump order and content dwell order in the user path behavior chain structure, extract the sequence number of the jump nodes in the path segment, read the number of the number interval, divide the number sequence into segments according to the numerical segment to which the number of interval belongs, and obtain the jump rhythm number segment table.

[0022] S202: Based on the jump rhythm sequence number segment table, extract the sequence number of the content dwell node within the path segment, read the number of spacing between adjacent numbers, and map the number of spacing to the segment position in the rhythm segment table to obtain the content dwell sequence number correspondence table.

[0023] S203: Based on the content dwell sequence number correspondence table, identify the path segment index number that maintains the same trend of change during the sequence number change process, and track the index number according to the original location of the path segment in the behavior chain to obtain the inertial feature path segment sequence.

[0024] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0025] S301: Based on the path segment number in the inertial feature path segment sequence, retrieve the content node with the corresponding number from the behavior chain, read the associated content node sequence number, track the original position interval of the sequence number in the behavior chain, and locate the node sequence number position according to the position interval to obtain the content node interval sequence number set.

[0026] S302: Based on the content node interval sequence number set, read the corresponding node sequence number according to the path segment number sequence, match the node sequence number with the path segment sequence number, extract the continuously occurring node sequence number chain, monitor the link order of the sequence number in the chain, and obtain the node sequence number link table.

[0027] S303: Based on the node sequence number link table, trace the corresponding position of the node sequence number in the link table, and the correspondence between the position order of the docking nodes in the path segment and the path segment number to obtain the content node path structure.

[0028] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0029] S401: Based on the content node path structure, extract the content type field, tag field, and interaction level field of the node; compare the value format in the field to identify the node items with the same field value; and aggregate the corresponding node number to obtain the set of node numbers corresponding to the field.

[0030] S402: Based on the node number set corresponding to the field, extract the path segment number corresponding to each node number in the path labeling structure, connect the node number and path segment number fields, and fill the node number into the number sequence in sequence to obtain the node path segment connection sequence.

[0031] S403: Based on the node path segment docking sequence, read the order of nodes in the path structure, synchronously track the sequential direction of the path segment numbers, and write the tracked numbering relationship to the end of the sequence to obtain the combination of behavior chain operation content.

[0032] As a further aspect of the present invention, in the process of extracting the path segment number corresponding to each node number in the path labeling structure based on the field corresponding to the node number set, the repeated occurrence of the node number in the path labeling structure is identified, and the path segment number and the node number are extracted to correspond.

[0033] During the process of connecting the node number and path segment number fields and sequentially filling the node number into the number sequence, an order identifier is set according to the order of the path segment numbers in the path structure, and the order identifier is used to guide the order in which the node numbers are filled into the number sequence.

[0034] During the process of reading the order of nodes in the path structure, the reading order is based on the number corresponding to the sequence identifier, and the order information of the path segment number in the node path segment docking sequence is synchronized. The read number relationship is then appended to the end of the behavior chain operation content combination.

[0035] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0036] S501: Based on the combination of the operation content of the behavior chain, extract the path number corresponding to the content cluster, retrieve the access frequency field and node level field associated with the number in the path structure, and map the field value to the path number to obtain the path access field mapping set.

[0037] S502: Based on the path access field mapping set, read the access frequency value associated with the number in the path number sequence and the node level number, and connect the field sequence according to the path number order to obtain the path node field sequence group;

[0038] S503: Based on the field change characteristics in the path node field sequence group, extract the operation point index whose frequency value is in the middle segment of the behavior chain trigger frequency in the node level position, and match the index sequence with the task index item in the operation task set one by one to locate the task pointer and obtain the operation execution plan.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] In this invention, a path behavior chain including jump order and dwell nodes is constructed, a rhythmic and coherent inertial path sequence is extracted, and a combination relationship between path and content is established by combining path number and content node hierarchy and distribution. Node tags and path aggregation features are summarized, and operation points are set according to frequency and density to promote the linkage between behavior trajectory, content structure and path execution, thereby enhancing the correspondence between operation strategy and user path. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the steps of the present invention;

[0043] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0044] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0045] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0046] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0047] Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0049] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0050] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0051] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0052] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0053] Please see Figure 1 This invention provides a method for user behavior analysis and refined operation based on big data, including the following steps:

[0054] S1: Obtain the page jump order, content access node order, and path time sequence tag in the user's continuous operation. Connect the page jump structure and node access information in sequence. Combine the jump nodes and dwell nodes in the path segment into continuous behavior segments and bind the time sequence field and node structure number to obtain the user path behavior chain structure.

[0055] S2: Based on the continuous jump order and content dwell order in the user path behavior chain structure, identify path segments with similar jump patterns, analyze the rhythm changes of path jumps, the coherence of dwell order and the correlation of jump content, and filter path segments with consistent behavior patterns according to the path order in the behavior chain to obtain the inertial feature path segment sequence.

[0056] S3: Based on the path segment number in the inertial feature path segment sequence, extract the content node corresponding to the path number in the behavior chain, match the content node with the path segment number, associate each content node with its own path segment according to the path order, and locate the position of each content node in the path segment to obtain the content node path structure.

[0057] S4: Based on the node content field in the content node path labeling structure, identify content nodes with similar structures according to the content type, tags and interaction level, pair each content node with the corresponding path segment, and match the content nodes with the path according to the order in the path to obtain the behavior chain operation content combination.

[0058] S5: Based on the distribution of content clusters in the content combination group of the behavior chain operation in the user path structure, according to the path type, access frequency and node level, the differentiated user groups are associated with the corresponding operation tasks, and operation points are set on the path according to the behavioral characteristics to obtain the operation execution plan.

[0059] The user path behavior chain structure includes page jump structure, node access information, continuous behavior segments, time sequence field, and node structure number. The inertial characteristic path segment sequence includes path segments with similar jump forms, path jump rhythm, continuity of dwell order, correlation of jump content, and path segment number. The content node path structure includes path segment number, content node, node path position, and path sequence identifier. The behavior chain operation content combination includes node content field, content type, content tag, interaction level, and content path pairing relationship. The operation execution plan includes user group type, corresponding operation task, path operation point, and behavior characteristic tag.

[0060] Please see Figure 2 The specific steps of S1 are as follows:

[0061] S101: Obtain the page jump order, content access node order, and path time sequence tag; aggregate jump nodes and dwell nodes according to time tag; verify the order of the aggregated node sequence; and obtain the node sequence structure frame.

[0062] First, extract the jump order list, access node sequence number column, and path tag number column. The jump order can be read from the page jump record field in the user behavior log according to the number order. The access node order can be obtained from the content number pointed to by each click. The path time tag corresponds to the path number field recorded in the same session segment in the behavior log. In the implementation process, for example, a jump order is [A→B→C], the corresponding access node number is [21, 43, 78], and its path tag number can be [5, 5, 6]. At this time, nodes with the same path tag should be divided into the same sequence segment, and the access node and jump node should be sequence-bound according to the path tag value. Then, the intermediate process is to perform alignment processing on the combination of jump node and dwell node, that is, extract the jump node source number and the corresponding dwell node target number to establish a one-to-one jump-dwell mapping pair. For example, if the jump path is B to C, then the jump node is B, and the corresponding dwell node is C. For example, if the two nodes are numbered 43 and 78 respectively and are both in the path with a tag value of 6, during subsequent processing, the node number set is serialized according to the path number field. The continuity requirement of the jump sequence under the same tag number is checked. Specifically, the difference between the order of the numbers can be compared to determine whether the node is a preceding or following node in the access sequence. If the numbers are always increasing, the order is considered reasonable. If there are jump numbers or back jump numbers, the tag segment should be removed or interrupted. For example, if the node sequence under a certain path number is [21, 43, 78], but the jump order record in the behavior record is [78→21], it means that the path behavior is not continuous and should be logically split. Then, the remaining part is added to the next sequence according to the tag number and the number order is checked. After completing the complete order verification of the jump order and the sequence number of the stopped node, the jump sequence and its path number can be mapped and indexed to finally obtain the node sequence structure frame.

[0063] S102: Based on the node sequence structure frame, according to the page jump structure and node access information in the sequence, the time sequence field and structure number of the corresponding nodes are compared, the number mapping segment relationship after comparison is divided, and the node segment mapping matrix is ​​obtained.

[0064] First, the node number pairs in the page jump sequence need to be obtained. For example, if node A jumps to B, and B jumps to C, the resulting number sequence is [11, 27, 45]. Then, the content access information corresponding to these numbers is extracted, namely the type, level, and tag value of the accessed node, for subsequent field comparison. Next, the time sequence field corresponding to each jump is read from the behavior chain log. For example, the time for jump number 11 is t1, number 27 is t2, and number 45 is t3. These three time fields are converted into time sequence number fields [1, 2, 3] and matched one-to-one with the jump number sequence to obtain the sequence number correspondence. After obtaining the sequence number correspondence, the structure number field is read, which is the path number value of each node in the original path structure. For example, if numbers 11, 27, and 45 are located in path numbers 5, 5, and 6 respectively, it is necessary to determine whether there is a cross-segment phenomenon in the structure number when the node sequence shows a continuous upward trend in the time field. If the structure number corresponding to adjacent time sequence numbers is different, the current node sequence needs to be split into two segments before and after the path number change. The points are sequenced and incorporated into independent numbered segments. For example, nodes 11 and 27 with the same path number in time sequence [1, 2] are grouped into the same segment, corresponding to segment number A, while node 45, as a path number change point, is assigned a separate segment number B. Then, each numbered segment is indexed to generate a correspondence matrix between jump number, time sequence number, and path number. The entire process does not involve calculation of jump content; number extraction and number mapping are based solely on the consistency of field values ​​and positional order. If reversed numbers are found in the time sequence field, such as node number [11, 27, 45] but time number [3, 2, 1], the sequence must be skipped and not written into the number mapping dataset. At the same time, during the field extraction stage, the comparison between number fields should be used to determine whether there is a case of duplicate numbering between time number and structure number. If so, the duplicates should be removed to ensure that each group of time number fields corresponds to a unique structure number field, and finally, the node segment mapping matrix is ​​obtained.

[0065] S103: Based on the node segment mapping matrix, perform sequence connectivity judgment on the combination state of jump nodes and stay nodes in the matrix, connect the sequence set path links in the connected state according to the jump order, and obtain the user path behavior chain structure.

[0066] First, extract the path segment number, node number, and sequence index number of the preceding and following nodes in each jump pair. The jump node can be considered the starting page number, and the stopping node can be considered the target page number. For example, a jump pair from node number 34 to node number 56 belongs to path segment number 3 and path segment number 3, respectively. It's necessary to confirm whether the two nodes in the jump path exist in the same numbering segment and whether the jump direction matches the original sequence's numbering order. This is determined by reading the node index numbers in the sequence. For example, if node 34's index is 12 and node 56's index is 13, it's considered a forward connected state. If the numbering is reversed, the combination cannot be considered a continuous jump sequence. After determining all jump pairs, extract all node pairs in a connected state to form a connected node linked list. For example, multiple jump pairs might be 34 to 56, 56 to 78, and 78 to 90. If all meet the connectivity order criteria, a link order can be established in the form 34→56→78→90. The next step is to process this... The path connection order between these linked lists is determined by judging the position of each linked list in the original jump sequence. The linked lists are connected in order based on the index position of the first occurrence of its starting node. For example, if linked list one starts with node 11 at index 8 and linked list two starts with node 34 at index 15, then linked list one should be placed before linked list two. The linked lists are connected based on this order to form a complete path jump sequence. In the process of constructing the jump path, it is also necessary to judge whether there are duplicate nodes in the jump path. If the same node number appears in different linked lists, only the node number of the first occurrence is retained, and the subsequent node numbers are not included in the path link splicing process. In addition, for cases where jump pairs fail to form a connected state, they should be treated as path breakpoints and not included in the path splicing. After the jump path splicing is completed, the resulting sequence is the complete sequence composed of all connected path links in the jump order. This sequence is used to further bind the subsequent path content nodes with the operational content docking information, and finally obtain the user path behavior chain structure.

[0067] Please see Figure 3 The specific steps of S2 are as follows:

[0068] S201: Based on the jump order and content dwell order in the user path behavior chain structure, extract the sequence number of jump nodes in the path segment, read the number of intervals between the numbers, divide the sequence number into segments according to the numerical segment to which the intervals belong, and obtain the jump rhythm sequence number segment table.

[0069] First, extract the sequence of jump node numbers from the path segment. These numbers typically correspond to the order in which pages appear in the user's behavior chain. For example, if a user visits pages 12, 14, 20, and 23 in sequence, the resulting jump node sequence is 12, 14, 20, and 23. This sequence is then input as an integer array into the subsequent processing flow. In a real-world e-commerce scenario, for example, if a user navigates from the homepage to a product page and then to the checkout page, the possible sequence might be 5, 9, and 18. These numbers can be used to reflect the progression of the user's browsing behavior. To further analyze the user's rhythm, calculate the difference between each pair of adjacent numbers and determine the corresponding "spacing." For example, the spacing between 14 and 20 is 6, and the spacing between 20 and 23 is 3. After extracting the spacing of all adjacent nodes sequentially, a spacing sequence is formed, which is used for subsequent rhythm segment division. To support the rhythm judgment logic, the spacing values ​​need to be segmented according to empirical rules. For example, spacings of 1 to 3 belong to segment A, 4 to 7 to segment B, and 8 to 12 to segment C. Each segment represents a different pace of user browsing behavior: segment A represents fast pace, B represents medium pace, and C represents slow pace. In a practical example, if the spacing sequence is 2, 6, 3, 10, it can be mapped to the segment sequence A, B, A, C. Next, the original jump sequence is mapped one-to-one with the segment sequence, so that each jump node is associated with a rhythm segment identifier. The specific judgment process determines the segment by comparing the difference between each pair of preceding and following nodes, and assigns a rhythm segment identifier accordingly. For example, the difference between nodes 12 and 14 is 2, belonging to segment A; the difference between 14 and 20 is 6, belonging to segment B; and the difference between 20 and 23 is 3, belonging to segment A. These rhythmic segments are arranged sequentially to form a rhythmic sequence. The continuity of user behavior rhythm can be determined by comparing the consistency of adjacent segments within the rhythmic sequence. If consecutive identical segments appear (e.g., two consecutive "A"s), it indicates a continuation of the rhythm; if segments change, it indicates a change in rhythm. This rhythmic change pattern can serve as the basis for constructing the segment table. After completing the labeling of all segments, the jump node sequence and its corresponding rhythmic segments need to be combined into a tabular data structure to obtain the jump rhythm sequence segment table.

[0070] S202: Based on the jump rhythm sequence number segment table, extract the sequence number of the content dwell node within the path segment, read the number of spacing between adjacent numbers, and map the number of spacing to the segment position in the rhythm segment table to obtain the content dwell sequence number correspondence table.

[0071] First, clarify the method for extracting node numbers. Node numbers originate from the access action numbers in user dwell behavior. For example, if a user visits the product details page, review page, and recommendation page, the node numbers generated are 22, 24, and 29 respectively, then the sequence number is 22, 24, 29. After extracting this sequence, the distance between adjacent node numbers needs to be read. The distance is the difference between two node numbers. According to the above sequence, 24 minus 22 equals 2, and 29 minus 24 equals 5. The distance read is 2 and 5, corresponding to the behavioral progression distance between two adjacent nodes. Subsequently, each distance read needs to be mapped to a preset segment position in the jump rhythm sequence number segment table. The jump rhythm segment table needs to be pre-configured with segment classification labels corresponding to different distance values. For example, distance values ​​in the range of 1 to 3 are classified as label A, distance values ​​in the range of 4 to 6 are classified as label B, and distance values ​​above 7 are classified as label C. In actual projects, this segment can be constructed by referring to typical user behavior data. In this example, spacing 2 should belong to label A, and spacing 5 to label B. The spacing values ​​are sequentially compared to their corresponding category labels in the rhythm segment table. This comparison establishes a mapping relationship between the number of each dwell node and its corresponding rhythm label. During processing, all spacing values ​​must be associated with the rhythm segment label one by one. No difference in number between any nodes can be omitted, and no two non-contiguous nodes should be mapped across regions. Furthermore, for adjacent nodes with the same number (i.e., a spacing of 0), they should be treated as invalid and not participate in the mapping operation. In the example, if the node sequence is 22, 22, 24, the spacing between the first 22 and the second 22 is 0, and no rhythm label binding is performed; they should be skipped directly. After all valid spacing is processed, the number of each content dwell node and its mapped rhythm label number are merged into the mapping relationship set. This set will serve as the basis for subsequent node rhythm continuity judgments, ultimately yielding the content dwell sequence number correspondence table.

[0072] S203: Based on the content dwell sequence number correspondence table, identify the path segment index number that maintains the same trend of change during the sequence number change, and track the index number according to the original location of the path segment in the behavior chain to obtain the inertial feature path segment sequence;

[0073] First, identify the path segment index numbers that maintain the same trend of change during the sequence number change process. When performing this operation, extract the content node number sequence corresponding to each path segment from the sequence number correspondence table, and compare the continuity of the change direction of adjacent numbers in each sequence. The change direction can be represented as increasing, decreasing, or unchanged. In a real-world scenario, for example, if the path segment number is P1, and its node sequence is 22, 24, 27, 29, with adjacent numbers continuously increasing, it indicates that this segment has a stable upward trend. If another path segment is numbered P2, its node sequence... If the point sequence is 31, 28, 25, 22, and the adjacent numbers continuously decrease, it is identified as a continuous downward trend. When determining the trend, the increment / decrement relationship of each jump number needs to be broken down, using a segment-by-segment comparison method rather than overall interval analysis. For example, in P1, 22 and 24 show positive changes, 24 and 27 show positive changes, and 27 and 29 show positive changes. Three consecutive changes in the same direction indicate a consistent trend. If opposite changes are inserted in the middle, it does not constitute a valid trend. Furthermore, for certain sequences such as 32, 32, 33, 33, 32... If the jump direction is unstable and there are multiple nodes with the same value, such a sequence should be judged as an invalid trend or cannot be included in the trend sequence set. After identification, the path segment numbers that meet the same trend change are temporarily stored as a trend path segment set, and their trend direction is recorded. In the actual example, P3, P5, and P7 can be set as path segments with the same increasing trend, thus forming the same trend set. Then, according to the original path order in the behavior chain, the position of the index number of these path segments needs to be tracked. This process requires calling the starting index of the path segment in the overall chain in the behavior chain record. For example, the starting index of the sequence corresponding to path segment P3 is 15, the starting index of P5 is 26, and the starting index of P7 is 33. Based on the index position, the sorting order of the path segments in the chain can be determined, and the path segments of the same trend are arranged into a group of continuous segments according to the original order of appearance. Finally, the path number sequence is cleaned on the basis of the segment column, removing the path segment numbers with order conflicts or trend reversals, and supplemented and adjusted through number mapping, finally obtaining the inertial feature path segment sequence.

[0074] Please see Figure 4 The specific steps of S3 are as follows:

[0075] S301: Based on the path segment number in the inertial feature path segment sequence, retrieve the corresponding number of the content node from the behavior chain, read the associated content node sequence number, track the original position interval of the sequence number in the behavior chain, and locate the node sequence number position according to the position interval to obtain the content node interval sequence number set.

[0076] First, extract the set of content nodes corresponding to the path segment numbers from the overall behavior chain. Content nodes refer to the page elements where the user stops or interacts within a certain path segment. Each node corresponds to a unique number. For example, if the path segment number is P9, the associated content nodes might be node numbers 44, 47, and 50. Extracting the path segment numbers can be achieved by matching the path identifier field in the behavior chain one by one with the generated inertial path segment sequence. During the matching process, it is necessary to check whether each path segment number is completely consistent with the path identifier field in the behavior chain. The identified content node numbers need to be read from the original behavior chain data table using a field filtering method. All relevant content node numbers are filtered out using the path segment number as the search condition. After extraction, it is also necessary to read the original position of each content node number in the overall behavior chain sequence. This position is not the content number itself, but rather its actual sequence index number in the chain. For example, if node number 44 appears at position 21 in the behavior chain, number 47 at position 22, and number 50 at position 24, then their sequence positions are as follows: Positions 21, 22, and 24 need to be retrieved one by one from the behavior chain data records by number. Aggregate scanning or position estimation operations cannot be used. After obtaining the original positions, a mapping record needs to be established between these position numbers and the corresponding node numbers. Then, the position interval is confirmed by combining the set of content node numbers covered by each path segment. That is, it is determined whether the position of each node is within the number range from the beginning to the end of a certain path segment. In actual execution, taking path segment number P9 as an example, assuming that its starting position in the behavior chain is 20 and its ending position is 25, if the position of its associated content node number is 21, 22, or 24, then all of them fall within this range and are considered valid matches. If the position of a node number is 18 or 26, it will not participate in the interval positioning process and needs to be removed from the subsequent calculation. After all valid node positions are confirmed, the node number is associated with its original position under the path segment number, and the set of all node numbers in the path segment that meet the position range is written into the sequence index result list according to the path segment dimension to obtain the content node interval sequence number set.

[0077] S302: Based on the content node interval sequence number set, read the corresponding node sequence number according to the path segment number sequence, match the node sequence number with the path segment sequence number, extract the consecutively occurring node sequence number chain, monitor the link order of the sequence number in the chain, and obtain the node sequence number link table.

[0078] First, a traversal reading operation is performed on the path segment number sequence. During this process, the node sequence data corresponding to each path segment number is extracted. These node sequences can be read one by one from the interval sequence set generated in the previous steps and added to a temporary cache list. Each path segment number corresponds to a set of node sequences. For example, the node sequences corresponding to path segment P12 are 43, 44, and 45, and the node sequences corresponding to path segment P13 are 46, 48, and 49. These node sequences are arranged sequentially according to the path segment number order. From this, consecutive node sequence chains are identified. During the identification operation, a jump threshold value of 1 must be set, that is, only when the difference between two adjacent node sequences is 1 is it considered continuous. If the difference exceeds 1, it is considered interrupted. For example, in the sequence 43, 44, and 45, the continuity is valid, but there is a break between 45 and 48, which needs to be processed separately. The continuous subsequences are extracted separately and then merged into the main chain cache area. Then, a sequence check operation is performed on each identified node sequence chain. This check requires comparing the numbering relationships of consecutive nodes in the sequence and determining whether there are any numbering reversals, duplicates, or jumps. For example, if the node number chain is 52, 53, 54, 55, 56, the order is consistent. If it is 52, 53, 51, 55, it means that there is a reverse segment 51 in the chain, which needs to be broken into two segments: 52, 53, and 55. In addition, for sequences like 55, 55, 56, duplicate numbers need to be removed during analysis to prevent positional shift issues. After the sequence check and cleaning are completed, all node number chains are arranged segment by segment according to the original path segment numbering order. Each processed chain segment is marked with its start and end positions and the total number of nodes. For example, if the node number chain is 57, 58, 59, it means that the start position is 57, the end position is 59, and the total number of nodes is 3. This method stabilizes the relative order of nodes in the behavioral chain. Finally, all processed node number chains are added to a list and indexed by the path segment number to obtain the node number link table.

[0079] S303: Based on the node sequence number link table, trace the corresponding position of the node sequence number in the link table, and the correspondence between the position order of the connected nodes in the path segment and the path segment number to obtain the content node path structure;

[0080] First, extract the node number sequence of each chain from the link table, and read the original position index of each node number in the behavior chain. For example, node number 31 is at position 17 in the behavior chain, number 32 is at position 18, and number 33 is at position 19. In a real-world scenario, if a user consecutively visits the product page, specification page, and review page, these pages can correspond to the above node numbers and positions. The extraction of position indexes must be done by reading each node number one by one according to its first appearance in the behavior chain; interval estimation or batch marking methods are not allowed. After reading, the node positions in each chain are sequentially matched in ascending order to determine whether the nodes appear sequentially within the range specified by the path segment number. The path segment number sequence must be read synchronously from the inertial path segment sequence. For example, if the path segment number is P11, and it covers the behavior chain position range from 17 to 23, then node positions 17, 18, and 19 in the link table all fall within its range, indicating that these nodes belong to path segment number P11. When performing position matching, the node position sequence number must be matched one by one with the path segment number. When a node is outside the path segment range, the binding between its position and the path segment number must be broken in the chain. For example, if node 34 is at position 25, it does not belong to the segment covered by P11 and needs to be removed from the corresponding sequence. Then, the remaining node positions are reordered and their continuity with the path segment numbers is confirmed, maintaining a one-to-one correspondence between node numbers and path segment numbers. During position tracking, duplicate node positions must also be handled. For example, if node number 32 appears twice in the chain, both times at position 18, such duplicates should be considered the same node position and should not be repeated during the docking process. To avoid interfering with the path segment position sequence, after processing, the entire path segment number sequence needs to be compared segment by segment according to its order in the behavior chain to ensure that the node sequence under each path segment number can be arranged according to the direction of the behavior chain. For example, if the path segment numbers P11, P12, and P13 appear at positions 17, 20, and 25 in the behavior chain, then the node sequence chain needs to be assigned to the corresponding path segment number in this order. Finally, all path segment numbers and their corresponding node sequence positions are sequentially merged according to the link progression to obtain the content node path structure.

[0081] Please see Figure 5 The specific steps of S4 are as follows:

[0082] S401: Based on the content node path structure, extract the content type field, tag field, and interaction level field of the node, compare the value format in the field, identify the node items with the same field value, and aggregate the corresponding node number to obtain the set of node numbers corresponding to the field.

[0083] First, read the content type field, tag field, and interaction level field. After extracting each field, check if its value has a standardized format. For example, the content type field could be "product details," "comments," or "recommendation information"; the tag field could be "main recommendation," "regular," or "interactive"; and the interaction level field could be "first level," "second level," or "third level." For field value extraction, the index position must be clearly defined. For example, the field with node number 28 corresponds to "product details," "main recommendation," and "first level." In this case, node number 28 is temporarily stored in a temporary mapping pool. Then, perform the same extraction process on all content nodes in the path structure. After extracting each field, compare the values ​​in all node field sets according to the field item values. Value comparisons must be performed independently according to field type; that is, content type fields are compared separately and not mixed with tag fields. The logic for determining equality between field values ​​should be based on complete character consistency. For example, "main recommendation" and "main recommendation" are the same, but "main recommendation" and "main recommended product" are different. Fuzzy matching or matching containing specific characters is not allowed. The method is used to determine if field values ​​are equal. If they are, the corresponding node number is included in the number sequence under that field value. For example, if the node numbers corresponding to the field value "Main Recommendation" are 28, 29, 35, and 37, then the node numbers from 28 to 37 are recorded and added to the set corresponding to that field value. The same operation is then performed on the interaction level field and the content type field. During this process, it should be noted that even if the field value appears relatively infrequently, it should not be ignored. For example, if "Third Level" only appears in two nodes, it should still be processed completely. After all field values ​​are compared, the mapping relationship between the field item value and its subordinate node numbers can be output in the form of a list. For example, the content type field item "Comment Section" contains node numbers 21, 24, and 25; the tag field "Regular" contains node numbers 20, 21, and 30; and the interaction level "Second Level" contains node numbers 21, 28, and 33. Through the above extraction, comparison, and number reading actions, the correspondence between field content and node numbers is located and collected, and finally, the set of node numbers corresponding to the field is obtained.

[0084] S402: Based on the node number set corresponding to the field, extract the path segment number corresponding to each node number in the path labeling structure, connect the node number and path segment number fields, and fill the node number into the number sequence in sequence to obtain the node path segment connection sequence.

[0085] First, each node number is read sequentially from the node number set to obtain its mapping relationship in the path calibration structure. After each read, the calibration information in the path calibration structure needs to be retrieved. The corresponding field is usually stored in key-value format, where the key is the node number and the value is the path segment number. For example, if the node number is 18 and the corresponding path segment number is 4, then a binding relationship is established between node number 18 and path segment number 4. The reading operation must start from the first number in the number set and proceed sequentially in ascending order. When reading the path segment number, the existence of the corresponding value in the field should be verified. For nodes with missing path segment numbers, they need to be supplemented from the path calibration structure before continuing processing. Next, the node numbers and path segment numbers are written in parallel into a new docking list. The writing order strictly follows the original arrangement order of the number set and the order of the node numbers or path segment numbers cannot be adjusted. For example, if the node number set is 12, 18, 23, 26, 29, and the corresponding path segment number is 4, then the node number is 18 and the corresponding path segment number is 4. If the path segment numbers are 2, 4, 4, 5, and 6, then "12-2", "18-4", "23-4", "26-5", and "29-6" are stored in the docking list in sequence. Then, the node number filling operation is performed. In the filling operation, the path segment numbers need to be traversed sequentially. When traversing the path segment numbers, check whether there are number gaps according to the corresponding node number field. For example, if there are node numbers 26, 28, and 29 under path segment number 5, and if node number 27 was not originally included in the field number set, but has a corresponding path segment number 5 in the path labeling structure, then 27 should be filled into the corresponding position. The filling order is based on the actual order of the node numbers in the original path segment number. For example, if 26, 27, 28, and 29 are a complete number chain, they should be overwritten. Finally, the set of all node numbers corresponding to each path segment number forms a bidirectional association between the path segment and the node number, resulting in the node path segment docking sequence.

[0086] S403: Based on the node path segment docking sequence, read the order of nodes in the path structure, synchronously track the sequential direction of path segment numbers, and write the tracked numbering relationship to the end of the sequence to obtain the combination of behavior chain operation content;

[0087] First, read the node numbers and corresponding path segment numbers recorded in the sequence one by one, extracting the arrangement order of the node numbers. By rearranging the positions of the node numbers in the original sequence, their sequential relationship in the path structure can be obtained. After each reading, it is necessary to check whether there are duplicate numbers in the adjacent positions before and after the node number. If so, they need to be removed to maintain the uniqueness of the numbers. At the same time, the first appearance position of the node in the original path segment number sequence is used as the main index to set the priority direction. The sequential arrangement of the nodes is synchronously followed in the path segment number direction. That is, the path segment number corresponding to each node should be compared with the overall path segment number order. For example, if the path segment number order is 3, 4, 6, 7, 9, and the node number path segment distribution is 15-4, 16-6, 18-3, 21-7, then the node order should be arranged according to the path segment number as 18, 15, 16, 21. Construct the sequence in the path segment number direction. The system maps the path segment numbers to a linked list, then performs directional consistency verification. For path segments with consistent directions, the relative positions are confirmed, and the comparison results are appended to the end of the docking sequence. When appending information, the directional numbers should be written into the extended column of the original sequence in the order of the path segment numbers. When writing in order, the original node number correspondence is preserved, and the relative positions between node numbers remain unchanged. If a path segment number jump occurs during directional tracking, the jump point number is marked at the corresponding position, and the path segment number is recorded continuously. For example, in path segments 3, 4, 6, and 9, if node number 16 jumps to path segment number 9, 16 should be marked as the jump point, and the rest should be recorded sequentially as part of the directional number sequence. Finally, all node numbers, path segment numbers, and directional numbers are written into the same data table, arranged in the original sequence order. Each row of data corresponds to a node number and its associated path segment and directional index, resulting in a combination of behavioral chain operation content.

[0088] Please see Figure 6 The specific steps of S5 are as follows:

[0089] S501: Based on the behavior chain operation content combination, extract the path number corresponding to the content cluster, retrieve the access frequency field and node level field associated with the number in the path structure, and map the field value to the path number to obtain the path access field mapping set.

[0090] First, the path numbers corresponding to each content cluster are read one by one from the collection. When reading the path numbers, they are broken down into independent number entries, each corresponding to a content cluster instance. For example, content cluster A is associated with path number 12, and content cluster B is associated with path number 15. Numbers 12 and 15 are used as the starting points for subsequent processing. Then, for each starting point number, the corresponding access frequency field and node level field are searched in the path structure. The access frequency field indicates the number of times a user accesses a particular path number; for example, for path number 12, the access count is read as 8 times. For path number 15, the number of visits is read as 3. After reading the number of visits, it is necessary to determine the range of values. The number of visits can be divided into ranges with 5 as the boundary. When the number of visits is less than 5, it is classified as a sparse access range, and when the number of visits is not less than 5, it is classified as a dense access range. For example, the number of visits is 8, which is a dense access range, and the number of visits is 3, which is a sparse access range. Then, the node level field is read. The node level field can be set to a value that represents the level of the page content display position. For example, a node level of 2 means that the content is located on a second-level page, and a node level of 4 means that the content is located on a fourth-level page. After reading the node level values, a validity check needs to be performed on the node level. For example, the allowed value range for the node level field can be set between 1 and 6 to check if the current level value is within the available range. If both node level 2 and node level 4 are within the range, they can be considered valid levels for subsequent processing. After reading the access frequency and node level, the access frequency field and node level field are written into the corresponding relationship table according to the path number, forming a pairing relationship between path number and access frequency, and simultaneously forming a pairing relationship between path number and node level field. For example, path number 12 corresponds to... The access frequency 8 corresponds to node level 2, and path number 15 corresponds to access frequency 3 and node level 4. To further strengthen the direct correspondence between field values ​​and path numbers, a mapping column needs to be added to the mapping table. The field content when the path number appears in each field is written into this column. For example, path number 12 is filled with 8 and 2 in the mapping column, and path number 15 is filled with 3 and 4 in the mapping column. The connection between all field values ​​and path numbers is written into the mapping table in the order of content clusters, so that each path number corresponds to a set of field value records, and finally the path access field mapping set is obtained.

[0091] S502: Based on the path access field mapping set, read the access frequency value associated with the number in the path number sequence and the node level number, and connect the field sequence according to the path number order to obtain the path node field sequence group;

[0092] First, read all the numbered items in the path number sequence. Each number represents a specific path location. For example, when the path number sequence is 8, 12, 15, 21, read the access frequency value and node level number corresponding to path numbers 8, 12, 15, and 21 one by one. When reading the access frequency value, it is necessary to call the field value corresponding to the path number in the mapping set. For example, number 8 corresponds to an access frequency of 6 times, number 12 to 3 times, number 15 to 9 times, and number 21 to 2 times. At the same time, read its node level. The access frequency values ​​are numbered as follows: number 8 corresponds to level 3, number 12 to level 2, number 15 to level 4, and number 21 to level 1. During the processing of access frequency values, these values ​​should be divided into several categories based on fixed segments. For example, access frequencies of 5 times or less can be classified as low-frequency, 5 to 10 times as medium-frequency, and more than 10 times as high-frequency. Correspondingly, numbers 12 and 21 are low-frequency, and numbers 8 and 15 are medium-frequency. Node level numbers are then ordered according to level depth; for example, level 1 represents the top-level content. Level 4 represents a deeper content layer. After reading, these access frequency values ​​are concatenated with the node level numbers in the order of the path numbers. Each set of data constitutes a complete set of fields. For example, number 8 corresponds to 6 accesses and level 3, number 12 corresponds to 3 accesses and level 2, and so on. After matching all the field values, the field values ​​corresponding to each number are concatenated in an orderly manner and combined into a field sequence according to the path number from smallest to largest. For example, numbers 8, 12, 15, and 21 are combined to form field sequence groups of 6-3, 3-2, 9-4, and 2-1, respectively. Each group consists of access frequency and level value. During the field combination process, to prevent field misalignment, the original order of the path numbers needs to be checked, that is, to confirm that the access frequency field and the level field accurately correspond to their path numbers. When there are duplicates or omissions in the path numbers, the fields need to be supplemented or an abnormal status needs to be marked. Finally, the access frequency fields of all path numbers and the node level fields are concatenated and combined in the order of the numbers to obtain the path node field sequence group.

[0093] S503: Based on the field change characteristics in the path node field sequence group, extract the operation point index whose frequency value is in the middle segment of the behavior chain trigger frequency in the node level position, and match the index sequence with the task index item in the operation task set to locate the task and obtain the operation execution plan.

[0094] First, read the continuous arrangement of each field item in the field sequence group. Each field item consists of an access frequency value and a node level number. For example, when the path number sequence is 10, 14, 18, 25, the field sequence group can be displayed as 5-2, 3-1, 8-3, 4-2, where the access frequency values ​​are 5, 3, 8, and 4 respectively, and the node level numbers are 2, 1, 3, and 2 respectively. When reading the fields, the numerical relationships within each field need to be broken down item by item. The access frequency values ​​need to be divided into segments. For example, access frequencies 1 to 4 are assigned to segment A. Access frequencies 5 to 8 are assigned to segment B, and access frequencies 9 to 12 are assigned to segment C. Therefore, access frequency 5 belongs to segment B, access frequency 3 to segment A, access frequency 8 to segment B, and access frequency 4 to segment A. Node hierarchy numbers can be numbered from 1 to 4 according to depth, with number 1 representing the top-level node and number 4 representing a deeper node. After reading, the changing trend shown by the field sequence is mapped to the interaction number of the nodes. When retrieving the sequence of interaction node numbers within a path segment, the interaction nodes are read in the order of their appearance. For example, if the interaction node numbers are 3, 3, 7, 7, 7, 11... When this happens, we should count the occurrences of number 3 (2 times), number 7 (3 times), and number 11 (1 time), and compare these frequencies with the corresponding field trends. If the frequency of access in the field sequence increases, and the corresponding interactive node also shows an increasing number of repetitions, it is considered that there is a synchronous sequence change relationship. For example, if number 7 appears 3 times, it shows the same trend as the frequency of access in the field increasing from 3 to 8. In this case, the path number field corresponding to node number 7 needs to be connected. The path number 18 corresponding to number 7 should be written into the intermediate sequence, and this number should be consistent with the direction of field change. In this connection process, the node number and path number must be strictly matched to ensure that the original order of the number in the path number sequence can be accurately tracked. When writing the number relationship, the number is extended by appending to the end of the sequence. For example, 18 is appended to the end of the existing number sequence 10, 14, 18, 25 to form a further sequence chain. By processing all interactive node numbers and field trends item by item, the correspondence between node numbers and path number fields is completed, and the operation execution plan is finally obtained.

[0095] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for user behavior analysis and refined operation based on big data, characterized in that, Includes the following steps: S1: Obtain the jump order, access node and path time information in the user operation, connect the jump and stay nodes in sequence, bind the time field and node structure number to obtain the user path behavior chain structure; S2: Based on the user path behavior chain structure, identify similar jump path segments, analyze the jump rhythm, the continuity of the dwell order and the correlation between the content, filter path segments with consistent behavior patterns according to the path order, and obtain the inertial feature path segment sequence. S3: Based on the path segment number of the inertial feature path segment sequence, extract the corresponding content nodes, pair the nodes with the path segments according to the number, and locate the node position in the path segment to obtain the content node path structure. S4: Based on the content node path structure, according to node type, tag and interaction level, identify the content of nodes with the same structure, and obtain the combination of behavioral chain operation content by corresponding content nodes and paths in the order of path segments. S5: Based on the combination of operational content in the behavioral chain, analyze the relationship between differentiated user groups and operational tasks according to path type, access frequency and node level information, set path operation points with reference to behavioral characteristics, and obtain an operational execution plan.

2. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The user path behavior chain structure includes page jump structure, node access information, continuous behavior segments, time sequence field, and node structure number. The inertial feature path segment sequence includes path segments with similar jump forms, path jump rhythm, dwell sequence continuity, jump content relevance, and path segment number. The content node path structure includes path segment number, content node, node path position, and path sequence identifier. The behavior chain operation content combination includes node content field, content type, content tag, interaction level, and content path pairing relationship. The operation execution plan includes user group type, corresponding operation task, path operation point, and behavior feature tag.

3. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The so-called dwell node refers to the content page or interface node that a user visits and stays at a single moment in the jump path. The continuity of the dwell sequence refers to whether the time order in which users visit and stay at nodes is logically continuous.

4. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The interaction level refers to the display level of node content in the overall structure, and measures the level at which user behavior occurs in the operation process; The behavioral characteristics refer to the user's behavior patterns in the path, including access frequency, jump rhythm, dwell time, and interaction methods.

5. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the page jump order, content access node order, and path time sequence tag; aggregate jump nodes and dwell nodes according to time tag; verify the order of the aggregated node sequence; and obtain the node sequence structure frame. S102: Based on the node sequence structure frame, according to the page jump structure and node access information in the sequence, the time sequence field and structure number of the corresponding nodes are compared, and the number mapping segment relationship after comparison is divided to obtain the node segment mapping matrix. S103: Based on the node segment mapping matrix, perform sequence connectivity judgment on the combination state of jump nodes and stay nodes in the matrix, connect the sequence set path links in the connected state according to the jump order, and obtain the user path behavior chain structure.

6. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the jump order and content dwell order in the user path behavior chain structure, extract the sequence number of the jump nodes in the path segment, read the number of the number interval, divide the number sequence into segments according to the numerical segment to which the number of interval belongs, and obtain the jump rhythm number segment table. S202: Based on the jump rhythm sequence number segment table, extract the sequence number of the content dwell node within the path segment, read the number of spacing between adjacent numbers, and map the number of spacing to the segment position in the rhythm segment table to obtain the content dwell sequence number correspondence table. S203: Based on the content dwell sequence number correspondence table, identify the path segment index number that maintains the same trend of change during the sequence number change process, and track the index number according to the original location of the path segment in the behavior chain to obtain the inertial feature path segment sequence.

7. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the path segment number in the inertial feature path segment sequence, retrieve the content node with the corresponding number from the behavior chain, read the associated content node sequence number, track the original position interval of the sequence number in the behavior chain, and locate the node sequence number position according to the position interval to obtain the content node interval sequence number set. S302: Based on the content node interval sequence number set, read the corresponding node sequence number according to the path segment number sequence, match the node sequence number with the path segment sequence number, extract the continuously occurring node sequence number chain, monitor the link order of the sequence number in the chain, and obtain the node sequence number link table. S303: Based on the node sequence number link table, trace the corresponding position of the node sequence number in the link table, and the correspondence between the position order of the docking nodes in the path segment and the path segment number to obtain the content node path structure.

8. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the content node path structure, extract the content type field, tag field, and interaction level field of the node; compare the value format in the field to identify the node items with the same field value; and aggregate the corresponding node number to obtain the set of node numbers corresponding to the field. S402: Based on the node number set corresponding to the field, extract the path segment number corresponding to each node number in the path labeling structure, connect the node number and path segment number fields, and fill the node number into the number sequence in sequence to obtain the node path segment connection sequence. S403: Based on the node path segment docking sequence, read the order of nodes in the path structure, synchronously track the sequential direction of the path segment numbers, and write the tracked numbering relationship to the end of the sequence to obtain the combination of behavior chain operation content.

9. The user behavior analysis and refined operation method based on big data according to claim 8, characterized in that, In the process of extracting the path segment number corresponding to each node number in the path labeling structure based on the node number set corresponding to the field, the repeated occurrence of node numbers in the path labeling structure is identified, and the path segment number and node number correspondence are extracted. During the process of connecting the node number and path segment number fields and sequentially filling the node number into the number sequence, an order identifier is set according to the order of the path segment numbers in the path structure, and the order identifier is used to guide the order in which the node numbers are filled into the number sequence. During the process of reading the order of nodes in the path structure, the reading order is based on the number corresponding to the sequence identifier, and the order information of the path segment number in the node path segment docking sequence is synchronized. The read number relationship is then appended to the end of the behavior chain operation content combination.

10. The user behavior analysis and refined operation method based on big data according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the combination of the operation content of the behavior chain, extract the path number corresponding to the content cluster, retrieve the access frequency field and node level field associated with the number in the path structure, and map the field value to the path number to obtain the path access field mapping set. S502: Based on the path access field mapping set, read the access frequency value associated with the number in the path number sequence and the node level number, and connect the field sequence according to the path number order to obtain the path node field sequence group; S503: Based on the field change characteristics in the path node field sequence group, extract the operation point index whose frequency value is in the middle segment of the behavior chain trigger frequency in the node level position, and match the index sequence with the task index item in the operation task set one by one to locate the task pointer and obtain the operation execution plan.

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