Customer Behavior Recognition Method, Device, Computer Equipment and Medium

Abstract

The present invention discloses a method, device, computer device and medium for customer behavior recognition. By obtaining a transaction node label of a customer, where the transaction node label is a label associated with the current transaction node of the customer, obtaining a keyword library that matches the transaction node label, obtaining transaction information of the customer, where the transaction information includes research report information and news information, determining whether there is a keyword in the keyword library that matches the transaction information, if so, obtaining the quantity information of the successfully matched keyword, and matching a corresponding customer operation instruction according to the quantity information. It realizes that the system can accurately identify the best operation instruction for the customer at the current transaction node, solves the problem in the prior art that newly recruited foreign trade practitioners often have difficulty accurately judging the needs of customers due to insufficient work experience, resulting in a reduction in the success rate of order negotiation, and improves the work efficiency of foreign trade practitioners.

Description

Technical Field

[0001] The present invention relates to the technical field of behavior recognition, and particularly to a method, apparatus, computer device and medium for customer behavior recognition. Background Art

[0002] With the development of Internet technology and the change of customers' shopping concepts, online transactions have become a new type of trading method. Electronic intermediaries provide a new online trading platform for the multi-attribute (such as price, brand, after-sales service, etc.) commodity transactions between buyers and sellers. In the foreign trade industry, due to regional and time difference reasons, electronic intermediaries have become even more important means of communication in the foreign trade industry. The communication between buyers and sellers through electronic intermediaries has greatly promoted the development of China's foreign trade industry;

[0003] However, currently, such intermediary websites are mainly places for publishing transaction information, and do not recommend a suitable seller (or buyer) to the buyer (or seller), thus reducing the number of successful transactions to a certain extent, and directly affecting the economic benefits of the intermediary website. In fact, online transactions, especially B2B online transactions, can be a process of mutual evaluation and mutual selection of multi-attributes between buyers and sellers. For example, suppliers select distributors and distributors select suppliers. Their trading transactions are a two-way selection process of multi-attribute evaluation, a process of two-way matching decision-making. In the foreign trade industry, it is reflected that how to accurately judge the needs of customers in the process of customer communication has become an important skill for foreign trade practitioners. Newly recruited foreign trade practitioners often have difficulty accurately judging the needs of customers due to lack of work experience, resulting in a decrease in the success rate of order negotiation. Therefore, how to accurately identify the needs of customers and timely push corresponding processing guidelines to assist foreign trade practitioners in making judgments has become an urgent problem to be solved. Summary of the Invention

[0004] Based on this, in view of the above problems, there is a need to propose a customer behavior recognition method, apparatus, computer device and medium that can accurately identify the needs of customers and timely push corresponding processing guidelines to assist foreign trade practitioners in making judgments.

[0005] A customer behavior recognition method includes:

[0006] Obtaining a transaction node label of a customer, where the transaction node label is a label associated with the current transaction node of the customer;

[0007] Obtaining a keyword library that matches the transaction node label;

[0008] Obtaining transaction information of the customer, where the transaction information includes research report information and news information;

[0009] Determine whether there is a keyword in the keyword library that matches the transaction information;

[0010] If so, obtain the quantity information of the keywords that match successfully;

[0011] Match the corresponding customer operation instructions according to the quantity information.

[0012] A customer behavior recognition device, comprising:

[0013] A label acquisition unit, configured to acquire a transaction node label of a customer, where the transaction node label is a label associated with the current transaction node of the customer;

[0014] A matching unit, configured to acquire a keyword library that matches the transaction node label;

[0015] A transaction information acquisition unit, configured to acquire the transaction information of the customer, where the transaction information includes research report information and news information;

[0016] A judgment unit, configured to judge whether there is a keyword in the keyword library that matches the transaction information;

[0017] A quantity information acquisition unit, configured to, if so, acquire the quantity information of the keywords that match successfully;

[0018] An operation failure matching unit, configured to match the corresponding customer operation instructions according to the quantity information.

[0019] A computer device, comprising a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the following steps:

[0020] Acquire a keyword library that matches the transaction node label;

[0021] Acquire the transaction information of the customer, where the transaction information includes research report information and news information;

[0022] Judge whether there is a keyword in the keyword library that matches the transaction information;

[0023] If so, acquire the quantity information of the keywords that match successfully;

[0024] Match the corresponding customer operation instructions according to the quantity information.

[0025] A computer-readable medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to execute the following steps:

[0026] Acquire a keyword library that matches the transaction node label;

[0027] Obtain the transaction information of the customer, where the transaction information includes research report information and news information;

[0028] Determine whether there is a keyword in the keyword library that matches the transaction information;

[0029] If so, obtain the quantity information of the successfully matched keyword;

[0030] Match the corresponding customer operation instruction according to the quantity information.

[0031] The above customer behavior recognition method, device, computer device and medium obtain the transaction node label of the customer, perform keyword matching in the keyword library according to the transaction node label, and match the corresponding customer operation instruction according to the number of occurrences of the keyword in the keyword library, realizing that the system can accurately identify the best operation instruction for the customer at the current transaction node, enabling foreign trade practitioners to accurately judge the customer's needs according to the best operation instruction and communicate with the customer in the next step. It solves the problem in the prior art that new foreign trade practitioners often have difficulty accurately judging the customer's needs due to lack of work experience, resulting in a decrease in the success rate of order negotiation. It realizes accurately identifying the customer's needs and timely pushing the corresponding processing guidelines to assist foreign trade practitioners in making judgments, improving the work efficiency of foreign trade practitioners. Brief Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0033] Among them:

[0034] Figure 1 It is a flowchart of the customer behavior recognition method in an embodiment;

[0035] Figure 2 It is a schematic structural diagram of the customer behavior recognition device in an embodiment;

[0036] Figure 3 It is a structural block diagram of a computer device in an embodiment. Detailed Embodiments

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] As Figure 1 shown, a customer behavior recognition method includes:

[0039] S1. Obtain the transaction node label of the customer, where the transaction node label is a label associated with the current transaction node of the customer;

[0040] As described in the above steps, the background system obtains the transaction node label of the customer. The transaction node label is the name of each cooperation link node in the process of the user's cooperation with the company. The background system can judge which link the cooperation process between the customer and the company specifically reaches according to the transaction node label, so as to assist in identifying the customer behavior of the customer. Among them, the cooperation link nodes matched by the transaction node label include invitation to offer, offer, acceptance, and delivery, corresponding to the invitation to offer link, offer link, acceptance link, and delivery link respectively;

[0041] It can be understood that the cooperation link nodes matched by the transaction node label are not limited to invitation to offer, offer, acceptance, and delivery. The user can set the cooperation link nodes matched by the transaction node label by himself / herself, and the present invention does not limit this.

[0042] S2. Obtain the keyword library matched with the transaction node label;

[0043] As described in the above steps, the background system obtains the keyword library matched with the transaction node label. It can be understood that the keywords included in the keyword library matched by each transaction node label are all different. For example, the keywords included in the keyword library matched by the invitation to offer transaction node label include "high growth", "rapid growth", and "unicorn", etc. In addition, the keywords matched with each transaction node label can be set by the user himself / herself, and the present invention does not limit this.

[0044] S3. Obtain the transaction information of the customer, where the transaction information includes research report information and news information;

[0045] As described in the above steps, the back-end system obtains the transaction information, research report information, and news information of the customer. The obtaining method is to crawl the business information of the customer through a web crawler, and split the obtained transaction information into the research report information and the news information. The research report information is the annual reports of the customer for each year, and the news information is various news information of the customer;

[0046] It can be understood that the transaction information not only contains the research report information and the news information, and the content of the transaction information can be set by the user himself / herself, and the present invention does not make any limitation thereto.

[0047] S4. Determine whether there are keywords in the keyword library that match the transaction information;

[0048] As described in the above steps, the back-end system determines whether the keywords included in the keyword library are contained in the transaction information. For example: when the keywords are "high growth", "rapid growth", and "unicorn", the back-end system determines whether the words "high growth", "rapid growth", and "unicorn" are included in the research report information and the news information of the customer.

[0049] S5. If so, obtain the quantity information of the successfully matched keywords;

[0050] As described in the above steps, when the back-end system determines that there are keywords in the keyword library that match the transaction information, obtain the number of times each keyword appears, which is recorded as the quantity information of the keyword. For example: the back-end system determines that the research report information and the news information of the customer contain the keyword, and then the back-end system obtains the number of times the keyword appears in the research report information and the news information, and the result is: the keyword "high growth" appears 4 times, then the back-end system determines that the quantity information of the keyword "high growth" is 4 times.

[0051] S6. Match the corresponding customer operation instructions according to the quantity information;

[0052] As described in the above steps, the back-end system matches the corresponding customer operation instructions according to the number of times the keyword appears in the research report information and the news information. At this time, foreign trade practitioners can use the customer operation instructions as an operation guide to communicate with the customer subsequently. For example: the customer operation instruction is "increase the transaction price", then the foreign trade practitioner can discuss with the customer according to the instruction of "increase the transaction price" so as to increase the transaction price of both parties.

[0053] Through the above method, the present invention obtains the transaction node tags of customers, matches keywords in the keyword library according to the transaction node tags, and matches the corresponding customer operation instructions according to the number of occurrences of the keywords in the keyword library, so that the system can accurately identify the best operation instructions for the customer at the current transaction node, enabling foreign trade practitioners to accurately judge the needs of the customer according to the best operation instructions and communicate with the customer in the next step. This solves the problem in the prior art that newly recruited foreign trade practitioners often have difficulty accurately judging the needs of customers due to lack of work experience, resulting in a decrease in the success rate of order negotiation. It realizes accurately identifying the needs of customers and timely pushing corresponding processing guides to assist foreign trade practitioners in making judgments, thereby improving the work efficiency of foreign trade practitioners.

[0054] In one embodiment, step S6 specifically includes:

[0055] S61. Obtain a customer operation instruction library, which stores operation instructions and the corresponding quantity ranges for matching the operation instructions. Match the corresponding quantity range according to the quantity information, and record the operation instruction corresponding to the quantity range as the customer operation instruction.

[0056] As described in the above embodiment, the background system obtains a customer operation instruction library, which stores operation instructions and the corresponding quantity ranges for matching the operation instructions. The quantity range is used to record the operation instruction matching the quantity range as the customer operation instruction when the quantity information is within the quantity range. For example: the keyword is "high growth", the quantity range matching "high growth" is 4 - 6, and the operation instruction matching the quantity range is "increase the transaction price";

[0057] If the background system determines that the number of occurrences of the keyword "high growth" is 5 times, meeting the requirements of the quantity range, then the background system records the operation instruction "increase the transaction price" as the customer operation instruction;

[0058] Through the above method in this embodiment, it realizes accurately matching the customer operation instruction according to the quantity range of the keyword in the customer operation instruction library, improves the accuracy of matching the customer operation instruction, and prevents operation mistakes of foreign trade practitioners caused by incorrect matching of the customer operation instruction.

[0059] In one embodiment, after step S4, it further includes:

[0060] S41. If so, obtain the number of occurrences of the keyword, obtain the keyword with the most occurrences, record it as the target keyword, and match the corresponding customer operation instruction according to the target keyword.

[0061] As described in the above embodiments, the background system obtains the occurrence times of each of the keywords, sorts the keywords according to the number of characters in the occurrence, and then the background system obtains the keyword with the highest rank after sorting, which is denoted as the target keyword. Then, the background system matches the corresponding customer operation instruction according to the target keyword. For example, the background system obtains that the keyword "high growth" appears 4 times in the transaction information, and the keyword "rapid growth" appears 3 times in the transaction information. Then, the background system determines the order of the keywords as: 1. The keyword "high growth"; 2. The keyword "rapid growth". Then, the background system designates the keyword "high growth" as the target keyword and matches the corresponding customer operation instruction according to the target keyword.

[0062] Through the above method, this embodiment realizes accurately identifying the target keyword according to the occurrence times of the keyword, and prevents the wrong push of the customer operation instruction caused by the mis-match of the target keyword.

[0063] In one embodiment, the matching of the corresponding customer operation instruction according to the target keyword specifically includes:

[0064] S42. Obtain the transaction value corresponding to the target keyword, and determine whether the transaction value exceeds a first preset threshold value matched with the transaction value. If so, match the corresponding customer operation instruction according to the target keyword.

[0065] As described in the above embodiments, if the transaction value is the financial value of the user, the background system obtains the transaction value corresponding to the target keyword and determines whether the transaction value exceeds the first preset threshold value matched with the transaction value. When the transaction value exceeds the first preset threshold value, the background system matches the corresponding customer operation instruction according to the target keyword. For example, the background system obtains that the transaction value matched with the keyword "high growth" is the return on net assets. Then, the background system obtains the specific value of the return on net assets from the research report information and determines whether the return on net assets is higher than the first preset threshold value matched with the return on net assets. If so, the background system matches the corresponding customer operation instruction according to the target keyword;

[0066] It can be understood that the transaction value can be other values except the financial value, and this application does not make any limitation thereto.

[0067] In this embodiment, through the above method, by obtaining the transaction value corresponding to the target keyword, and judging whether the target keyword meets the requirements according to the magnitude of the transaction value, and then matching the corresponding customer operation instruction according to the target keyword after determining whether the target keyword meets the requirements, it is possible to prevent the wrong push of the customer operation instruction caused by the mis-match of the target keyword.

[0068] In one embodiment, after obtaining the transaction value corresponding to the target keyword, the following steps are further included:

[0069] S43. Obtain a monitoring period, which is used to obtain the transaction value in the transaction information that matches the duration of the monitoring period, calculate the predicted value of the transaction value after the end of the monitoring period, and judge whether the predicted value of the transaction value is higher than a second preset threshold value that matches the transaction value. If so, use the target keyword corresponding to the transaction value to match the corresponding customer operation instruction.

[0070] As described in the above embodiment, the background system obtains a monitoring period, and obtains the initial value of the transaction value at the start of the monitoring period, and judges the predicted value of the transaction value according to the initial value of the transaction value and the duration of the monitoring period, denoted as the predicted value of the transaction value, and judges whether the predicted value of the transaction value is higher than the second preset threshold value that matches the transaction value. If so, use the target keyword corresponding to the transaction value to match the corresponding customer operation instruction. For example: The background system judges that the monitoring period is 3 days, then the background system obtains the initial value of the return on net assets of the customer company today (i.e., the transaction value), and judges the predicted value of the return on net assets of the customer company after 3 days according to the initial value of the return on net assets and the duration of the monitoring period (3 days), denoted as the predicted value of the transaction value, and judges whether the predicted value of the transaction value is higher than the second preset threshold value that matches the transaction value. If so, use the target keyword corresponding to the transaction value to match the corresponding customer operation instruction.

[0071] In this embodiment, through the above method, by calculating the predicted value of the transaction value, and then matching the corresponding customer operation instruction after the predicted value of the transaction value is higher than the preset threshold value, it is possible to evaluate the target keyword through the transaction value and then match the corresponding customer operation instruction according to the target keyword, further improving the matching accuracy of the customer operation instruction.

[0072] In one embodiment, the specific steps of calculating the predicted value of the transaction value after the end of the monitoring period include:

[0073] S44. Split the monitoring period into an acquisition period and a prediction period, obtain the transaction value after the end of the acquisition period, denote it as the reference transaction value, and calculate the predicted values of the transaction values at each time node within the prediction period based on the reference transaction value, the prediction period, and the acquisition period, and denote them as the predicted transaction values.

[0074] As described in the above embodiment, the background system splits the monitoring period into an acquisition period and a prediction period. Then, the background system obtains the transaction value after the end of the acquisition period, denotes it as the reference transaction value. Then, the background system calculates the predicted value after the end of the prediction period based on the reference transaction value, the prediction period, and the acquisition period, and denotes it as the predicted transaction value.

[0075] In this embodiment, through the above method, by splitting the monitoring period into an acquisition period and a prediction period and then predicting the transaction value corresponding to the target keyword, and finally judging the target keyword, the matching accuracy of the customer operation instruction is further improved.

[0076] In one embodiment, the calculating the predicted values of the transaction values at each time node within the prediction period based on the reference transaction value, the prediction period, and the acquisition period, and denoting them as the predicted transaction values specifically includes:

[0077] S45. Obtain the reference transaction value and denote the reference transaction value as the Y sequence value; denote the prediction period as the X sequence value; construct a linear regression line equation based on the X sequence value and the Y sequence value; calculate the predicted value of the Y sequence value after the end of the prediction period according to the linear regression line equation; and denote the predicted value of the Y sequence value as the predicted transaction value.

[0078] For example: if the monitoring period is 3 days, then the acquisition period is the 1st day (y ≤ 1), and the prediction period is the 2nd and 3rd days (2 < y ≤ 3);

[0079] The background system obtains the reference transaction value after the end of the acquisition period (i.e., after 1 day), then constructs a linear regression line equation based on the reference transaction value and the prediction period (2 < y ≤ 3) to obtain a line fitting equation, and then uses the line fitting equation to calculate the predicted value of the Y sequence value after the end of the prediction period (2 < y ≤ 3), and denotes the predicted value of the Y sequence value as the predicted transaction value.

[0080] In this embodiment, through the above method, by using a linear fitting equation to predict the transaction value corresponding to the target keyword and finally judging the target keyword, the matching accuracy of the customer operation instruction is further improved.

[0081] As Figure 2 shown, a customer behavior recognition device includes:

[0082] A label acquisition unit 1 for acquiring the transaction node label of the customer, where the transaction node label is a label associated with the current transaction node of the customer;

[0083] A matching unit 2 for acquiring a keyword library that matches the transaction node label;

[0084] A transaction information acquisition unit 3 for acquiring the transaction information of the customer, where the transaction information includes research report information and news information;

[0085] A judgment unit 4 for judging whether there is a keyword in the keyword library that matches the transaction information;

[0086] A quantity information acquisition unit 5 for, if so, acquiring the quantity information of the successfully matched keyword;

[0087] An operation failure matching unit 6 for matching a corresponding customer operation instruction according to the quantity information.

[0088] The above units are for implementing the above customer behavior recognition device, and will not be introduced one by one here.

[0089] Figure 3 shows the internal structure diagram of a computer device in an embodiment. The computer device may specifically be a server, and the server includes, but is not limited to, a high-performance computer and a high-performance computer cluster. As Figure 3 shown, the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the customer behavior recognition method. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can execute the customer behavior recognition method.

[0090] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0091] In one embodiment, the customer behavior recognition method provided in the present application can be implemented in the form of a computer program, and the computer program can run on a computer device as shown in Figure 3 The memory of the computer device can store each program template that makes up the customer behavior recognition device. For example, the label acquisition unit 1, the matching unit 2, the transaction information acquisition unit 3, the judgment unit 4, the quantity information acquisition unit 5, and the malfunction matching unit 6.

[0092] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0093] Obtain the transaction node label of the customer, where the transaction node label is a label associated with the current transaction node of the customer, obtain a keyword library that matches the transaction node label, the transaction information includes research report information and news information, obtain the transaction information of the customer, determine whether there is a keyword in the keyword library that matches the transaction information, if so, obtain the quantity information of the successfully matched keyword, and match the corresponding customer operation instruction according to the quantity information.

[0094] Combining the above embodiments, it can be seen that the greatest beneficial effect of the present invention is that by obtaining the transaction node label of the customer, performing keyword matching in the keyword library according to the transaction node label, and matching the corresponding customer operation instruction according to the number of occurrences of the keyword in the keyword library, the system can accurately identify the best operation instruction for the customer at the current transaction node, enabling foreign trade practitioners to accurately judge the customer's needs according to the best operation instruction and communicate with the customer in the next step. It solves the problem that new foreign trade practitioners often have difficulty accurately judging the customer's needs due to lack of work experience in the prior art, resulting in a decrease in the success rate of order negotiation. It realizes accurately identifying the customer's needs and timely pushing the corresponding processing guidelines to assist foreign trade practitioners in making judgments, improving the work efficiency of foreign trade practitioners.

[0095] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link

[0096] DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0097] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0098] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for identifying customer behavior, characterized in that, Including: Obtain the transaction node label of the customer, where the transaction node label is a label associated with the current transaction node of the customer; Obtain the keyword library that matches the transaction node label; Obtain the transaction information of the customer, where the transaction information includes research report information and news information; Determine whether there is a keyword in the keyword library that matches the transaction information; If so, obtain the quantity information of the successfully matched keyword; Match the corresponding customer operation instruction according to the quantity information.

2. The customer behavior recognition method according to claim 1, characterized in that The matching of the corresponding customer operation instruction according to the quantity information specifically includes: Obtain the customer operation instruction library, where the customer operation instruction library stores operation instructions and the quantity ranges that match the operation instructions; Match the corresponding quantity range according to the quantity information, Record the operation instruction corresponding to the quantity range as the customer operation instruction.

3. The customer behavior recognition method according to claim 1, wherein After determining whether there is a keyword in the keyword library that matches the transaction information, it further includes: If so, obtain the occurrence times of the keyword; Obtain the keyword with the most occurrence times, and record it as the target keyword; Match the corresponding customer operation instruction according to the target keyword.

4. The customer behavior recognition method according to claim 3, characterized in that The matching of the corresponding customer operation instruction according to the target keyword specifically includes: Obtain the transaction value corresponding to the target keyword; Determine whether the transaction value exceeds the first preset threshold that matches the transaction value; If so, match the corresponding customer operation instruction according to the target keyword.

5. The customer behavior recognition method according to claim 4, wherein After obtaining the transaction value corresponding to the target keyword, it further includes: Obtain the monitoring period, where the monitoring period is used to obtain the transaction value in the transaction information that matches the duration of the monitoring period; Deduce the predicted transaction value after the end of the monitoring period for the transaction value; Determine whether the predicted transaction value is higher than the second preset threshold that matches the transaction value; If so, use the target keyword corresponding to the transaction value to match the corresponding customer operation instruction.

6. The customer behavior recognition method according to claim 5, wherein, The deduction of the predicted transaction value after the end of the monitoring period for the transaction value specifically includes: Split the monitoring period into an acquisition period and a prediction period; Obtain the transaction value after the end of the acquisition period, and record it as the reference transaction value; Deduce the predicted transaction values at each time node in the prediction period according to the reference transaction value, the prediction period, and the acquisition period, and record it as the predicted transaction value.

7. The customer behavior recognition method according to claim 6, characterized in that, The deduction of the predicted transaction values at each time node in the prediction period according to the reference transaction value, the prediction period, and the acquisition period, and record it as the predicted transaction value, specifically includes: Obtain the reference transaction value, and record the reference transaction value as the Y sequence value; Record the prediction period as the X sequence value; construct a linear regression line equation according to the X sequence value and the Y sequence value; Deduce the budget value of the Y sequence value after the end of the prediction period according to the linear regression line equation; Record the budget value of the Y sequence value as the predicted transaction value.

8. A customer behavior recognition device, characterized in that, Including: A label acquisition unit for acquiring a transaction node label of a customer, where the transaction node label is a label associated with the customer's current transaction node; A matching unit for acquiring a keyword library that matches the transaction node label; A transaction information acquisition unit for acquiring the customer's transaction information, where the transaction information includes research report information and news information; A judgment unit for judging whether there is a keyword in the keyword library that matches the transaction information; A quantity information acquisition unit for, if so, acquiring the quantity information of the keywords that match successfully; An operation failure matching unit for matching corresponding customer operation instructions according to the quantity information.

9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, it implements the customer behavior recognition method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the customer behavior recognition method according to any one of claims 1 to 7.

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