Business product information analysis method and device, computer equipment and storage medium

Abstract

The invention relates to a business product information analysis method and device, computer equipment and a storage medium. The method comprises the steps of collecting business product information of a preset business field, and performing text extraction on the business product information to obtain unstructured text data; performing structured processing on the unstructured text data to obtain structured data; and extracting key fields from the structured data according to a preset analysis strategy to obtain analyzed structured data. By adopting the method, the product information can be efficiently and accurately analyzed.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, computer equipment and storage medium for analyzing business product information. Background Art

[0002] As the financial industry becomes increasingly competitive, financial companies need to continuously monitor and analyze products and strategies launched in the market to quickly respond to market changes. To achieve this goal, automated product information parsing and analysis technology is particularly important.

[0003] In traditional technology, product information analysis mainly relies on manual methods, specifically collecting data from industry reports, official website announcements and other channels, and then analyzing and processing by professionals. However, this method is inefficient, information acquisition is delayed, and the quality of data analysis is greatly affected by human factors and prone to errors. Competition in the financial market is becoming increasingly fierce, and the number of products defined by different financial companies has also shown a blowout growth. In order to strengthen the understanding of product information, keep abreast of product trends, and enhance industry competitiveness, a solution that can efficiently and accurately analyze product information is urgently needed. Summary of the invention

[0004] Based on this, it is necessary to provide a business product information parsing method, device, computer equipment and storage medium that can efficiently and accurately parse product information in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for parsing business product information, comprising:

[0006] Collecting business product information of a preset business field, performing text extraction on the business product information, and obtaining unstructured text data;

[0007] Performing structured processing on the unstructured text data to obtain structured data;

[0008] Key fields are extracted from the structured data according to a preset parsing strategy to obtain parsed structured data.

[0009] In one of the embodiments, the business product information corresponds to multiple file types, and the multiple file types include a first file type;

[0010] The extracting text from the business product information to obtain unstructured text data includes:

[0011] In a case where the business product information of the first file type is in a preset format, converting the business product information of the first file type into an image;

[0012] Character recognition is performed on the image to obtain unstructured text data.

[0013] In one embodiment, the method further comprises:

[0014] When the business product information of the first file type is not in a preset format, text data in the business product information of the first file type is extracted to obtain unstructured text data.

[0015] In one embodiment, the business product information corresponds to multiple file types, and the multiple file types include a second file type;

[0016] The extracting text from the business product information to obtain unstructured text data includes:

[0017] Character recognition is performed on the business product information of the second file type to obtain unstructured text data.

[0018] In one embodiment, the step of structuring the unstructured text data to obtain structured data includes:

[0019] Performing word segmentation processing on the unstructured text data through a natural language model to obtain product word segmentation;

[0020] Performing part-of-speech tagging on the product segmentation using the natural language model to obtain the tagged product segmentation;

[0021] The natural language model is used to perform syntactic analysis on the annotated product segmentation to obtain structured data.

[0022] In one embodiment, the method further comprises:

[0023] In the case where the parsed structured data is incomplete, determining an incomplete data segment;

[0024] Matching the incomplete data segment with the business product information library corresponding to the preset business field to determine compensation data;

[0025] The incomplete data segment is compensated according to the compensation data.

[0026] In one embodiment, the method further comprises:

[0027] The parsed structured data and the business product information are visually displayed.

[0028] In a second aspect, the present application also provides a business product information parsing device, including:

[0029] A text extraction module is used to collect business product information of a preset business field, perform text extraction on the business product information, and obtain unstructured text data;

[0030] A structuring module, used for performing structural processing on the unstructured text data to obtain structured data;

[0031] The key field extraction module is used to extract key fields from the structured data according to a preset parsing strategy to obtain parsed structured data.

[0032] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Collecting business product information of a preset business field, performing text extraction on the business product information, and obtaining unstructured text data;

[0034] Performing structured processing on the unstructured text data to obtain structured data;

[0035] Key fields are extracted from the structured data according to a preset parsing strategy to obtain parsed structured data.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0037] Collecting business product information of a preset business field, performing text extraction on the business product information, and obtaining unstructured text data;

[0038] Performing structured processing on the unstructured text data to obtain structured data;

[0039] Key fields are extracted from the structured data according to a preset parsing strategy to obtain parsed structured data.

[0040] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0041] Collecting business product information of a preset business field, performing text extraction on the business product information, and obtaining unstructured text data;

[0042] Performing structured processing on the unstructured text data to obtain structured data;

[0043] Key fields are extracted from the structured data according to a preset parsing strategy to obtain parsed structured data.

[0044] The above-mentioned business product information parsing method, device, computer equipment, storage medium and computer program product automatically collect business product information of preset business fields, perform text extraction on the business product information, and obtain unstructured text data, thereby structuring the unstructured text data to obtain structured data, and then extract key fields from the structured data according to the preset parsing strategy to obtain parsed structured data, thereby realizing the automated parsing of business product information, effectively solving the problem of low efficiency and error-prone manual parsing of product information in the existing financial industry, and being able to parse product information efficiently and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 An application environment diagram of a business product information parsing method in an embodiment;

[0047] Figure 2 The following is a flowchart of a method for parsing business product information in one embodiment;

[0048] Figure 3 A schematic diagram of a process of extracting text from business product information to obtain unstructured text data in one embodiment;

[0049] Figure 4 is a structural block diagram of a business product information parsing device in an embodiment;

[0050] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The service product information parsing method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 sends a business product information parsing task to the terminal 102, and the terminal 102 collects business product information of a preset business field according to the business product information parsing task, extracts text from the business product information, and obtains unstructured text data. After that, the unstructured text data is structured to obtain structured data, and then the key fields of the structured data are extracted according to the preset parsing strategy to obtain the parsed structured data. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones and tablet computers. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0053] In an exemplary embodiment, Figure 2 As shown, a business product information parsing method is provided, which is applied to Figure 1 The terminal in is taken as an example to illustrate, including the following steps 202 to 206. Among them:

[0054] Step 202: collect business product information of a preset business field, perform text extraction on the business product information, and obtain unstructured text data.

[0055] The preset business domain refers to the same or similar business domain. For example, the preset business domain may be the financial domain. Unstructured data refers to data with irregular or incomplete data structure, no predefined data model, and data that is not convenient to be represented by a two-dimensional logical table of a database. Exemplarily, unstructured data may include text, images, etc.

[0056] Optionally, a compressed package file containing business product information is obtained from multiple channels, and the compressed package file is decompressed to obtain the business product information. For example, the multiple channels may include industry reports, websites of financial enterprises, etc. The business product information may be a product file. The terminal may first perform text extraction on the business product information to obtain unstructured text data.

[0057] Optionally, the business product information may be collected according to the business field through a search engine, or may be collected from a database of a preset business field.

[0058] Step 204: perform structural processing on the unstructured text data to obtain structured data.

[0059] Among them, structured data refers to data organized according to a predefined data model, usually stored in a relational database, and can be queried and operated using structured query language.

[0060] Optionally, in order to facilitate the parsing of the business product information, after the unstructured text data is acquired, the unstructured text data is structured to convert the original business product information into structured data that is easy to parse.

[0061] Step 206: extract key fields from the structured data according to a preset parsing strategy to obtain parsed structured data.

[0062] Among them, the preset parsing strategy refers to the parsing rules and business field standards formulated for product specifications and formats.

[0063] Optionally, the terminal extracts key fields from the structured data based on a preset parsing strategy, thereby parsing the business product information, and determines the extracted key fields and structured data as parsed structured data. Exemplarily, the extracted key fields may include product name, channel range, design type, responsibility type, etc.

[0064] Optionally, the preset parsing strategy may be dynamically configured according to different product specifications and formats to adapt to different product specifications and formats.

[0065] In the above-mentioned business product information parsing method, business product information of a preset business field is automatically collected, text extraction is performed on the business product information to obtain unstructured text data, and then the unstructured text data is structured to obtain structured data, and then key fields are extracted from the structured data according to a preset parsing strategy to obtain parsed structured data, thereby realizing automated parsing of business product information, effectively solving the problem of low efficiency and error-prone manual parsing of product information in the existing financial industry, and being able to parse product information efficiently and accurately.

[0066] In an exemplary embodiment, the business product information corresponds to multiple file types, and the multiple file types include a first file type and a second file type. The first file type refers to a PDF file type, and the second file type refers to a file type in a non-PDF format, for example, the second file type may include Excel, images, etc.

[0067] In one exemplary embodiment, Figure 3 As shown, text extraction is performed on business product information to obtain unstructured text data, including:

[0068] Step 302: When the business product information of the first file type is in a preset format, convert the business product information of the first file type into an image.

[0069] Step 304: perform character recognition on the image to obtain unstructured text data.

[0070] The preset format refers to a format that cannot be directly parsed through PDF and converted into unstructured file data. Exemplarily, the preset format may be a first file type in a scanned image format.

[0071] Optionally, it is identified whether the business product information of the first file type is in a preset format. If the business product information of the first file type is in a preset format, it cannot be directly converted into a non-structured file data format through PDF parsing, and the business product information of the first file type needs to be converted into an image through a PDF parsing tool. Then, character recognition is performed on the image through optical character recognition (OCR) to obtain non-structured text data.

[0072] Furthermore, the above method also includes: when the business product information of the first file type is not in a preset format, extracting text data from the business product information of the first file type to obtain unstructured text data.

[0073] Wherein, when the business product information of the first file type is not in a preset form, it indicates that the first file type can be directly converted into a form of unstructured file data through PDF parsing. Exemplarily, the business product information of the first file type is not the first file type in the form of a scanned image. Text data can be directly extracted from the business product information of the first file type through a PDF parsing tool to obtain unstructured text data.

[0074] In this embodiment, when the business product information of the first file type is in a preset form, the business product information of the first file type is converted into an image, and character recognition is performed on the image to obtain unstructured text data. When the business product information of the first file type is not in a preset form, the text data in the business product information of the first file type is directly extracted to obtain unstructured text data. It is possible to select an appropriate unstructured text data extraction method according to the form of the business product information of the first file type, so as to achieve accurate extraction of unstructured data of business product information of the first file type in different forms.

[0075] In one exemplary embodiment, the business product information corresponds to multiple file types, and the multiple file types include a second file type; text extraction is performed on the business product information to obtain unstructured text data, including: character recognition is performed on the business product information of the second file type to obtain unstructured text data.

[0076] The second file type refers to a file type that is not in PDF format. For the business product information of the second file type, characters in the business product information of the second file type can be directly recognized by optical character recognition, thereby obtaining unstructured text data.

[0077] In this embodiment, character recognition can be directly performed for the second file type that is not in PDF format, which can improve the efficiency of extracting unstructured text data.

[0078] In an exemplary embodiment, unstructured text data is structured to obtain structured data, including: performing word segmentation processing on the unstructured text data through a natural language model to obtain product segmentations; performing part-of-speech tagging on the product segmentations through a natural language model to obtain tagged product segmentations; performing syntactic analysis on the tagged product segmentations through a natural language model to obtain structured data.

[0079] Optionally, after extracting the unstructured text data, the unstructured text data can be structured through a natural language model to obtain structured data. The process of structuring the unstructured text data through a natural language model may include: performing word segmentation on the unstructured text data through a natural language model to obtain product word segmentation. Among them, word segmentation refers to cutting a continuous text string into independent words or phrases. Product word segmentation refers to the word segmentation corresponding to business product information. Furthermore, the natural language model can perform word segmentation on the unstructured text data according to a preset business domain dictionary. Word segmentation can improve the ability to understand unstructured text data and avoid misunderstandings.

[0080] After obtaining the product segmentation, the product segmentation is tagged with parts of speech through the natural language model to obtain the tagged data. Part of speech tagging refers to identifying the grammatical role of each product segmentation in the business product information and determining the part of speech of each product segmentation, such as noun, verb, adjective, etc. Each segmentation is tagged according to its part of speech. Part of speech tagging helps to identify the grammatical structure of the sentence, such as subject, predicate, object, etc., thereby supporting syntactic analysis. By accurately marking the parts of speech, the semantic connotation of the sentence can be understood more clearly, helping to understand the role and relationship of vocabulary in the sentence, and thus supporting more complex language analysis tasks. Part of speech tagging can also guide the natural language model to generate text that conforms to grammatical rules, ensuring that the generated text is natural and fluent. At the same time, for some polysemous words, part of speech tagging can eliminate their ambiguity through contextual information and help to understand the meaning of the word more accurately.

[0081] After obtaining the annotated product segmentation, the annotated product segmentation is subjected to syntactic analysis through a natural language model to obtain structured data. Syntactic analysis refers to the analysis of the grammatical functions of words in a sentence.

[0082] Syntactic analysis can include dependency analysis and component analysis, etc. Through syntactic analysis, we can understand the structural relationship of sentences, determine the grammatical functions of words in sentences and the combination rules between words, and thus clarify the levels and components of sentences.

[0083] In this embodiment, by performing word segmentation, part-of-speech tagging and syntactic analysis on unstructured text data through a natural language model, the meaning of the unstructured text data can be accurately understood, thereby converting it into accurate structured data.

[0084] In an exemplary embodiment, the above method also includes: when the parsed structured data is incomplete, determining the incomplete data segment; matching the incomplete data segment with a business product information library corresponding to a preset business field to determine compensation data; and compensating the incomplete data segment according to the compensation data.

[0085] After obtaining the parsed structured data, the terminal can also perform compensatory parsing on the parsed structured data. The process of compensatory parsing may include: identifying whether the parsed structured data is complete, and determining the incomplete data segment when the parsed structured data is incomplete. Perform compensatory analysis on the incomplete data segment. Obtain a business product information library corresponding to a preset business field, in which standardized product information is stored. Match the incomplete data segment with the business product information library corresponding to the preset business field to determine the compensatory data. Among them, the compensatory data refers to the missing data. Compensate the incomplete data segment according to the compensatory data to obtain a complete data segment. Add the complete data segment to the parsed structured data for display.

[0086] In this embodiment, when the structured data after parsing is incomplete, the incomplete data segment is matched with the business product information library corresponding to the preset business field, the compensation data is determined, and the incomplete data segment is compensated according to the compensation data, which can improve the abnormal parsing results caused by incomplete data.

[0087] In an exemplary embodiment, the method further includes: visually displaying the parsed structured data and business product information.

[0088] After obtaining the parsed structured data, the parsed structured data and the initially collected business product information are visualized together. Exemplarily, the visualization method may include but is not limited to a line chart, a bar chart, a histogram, etc. By visually displaying the parsed structured data and the initially collected business product information, the association and trend between the data can be reflected. When the data is large and complex, the laws and patterns of the data, as well as the relationship between the various variables, can be more easily discovered through intuitive visualization. It can help companies make more accurate decisions, predict future trends, and optimize business processes.

[0089] Furthermore, the parsed structured data and the initially collected business product information may be transmitted to a backend system so that the backend system can perform subsequent processing.

[0090] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0091] Based on the same inventive concept, the embodiment of the present application also provides a business product information parsing device for implementing the business product information parsing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more business product information parsing device embodiments provided below can refer to the limitations of the business product information parsing method above, and will not be repeated here.

[0092] In an exemplary embodiment, Figure 4 As shown, a business product information parsing device is provided, including: a text extraction module 402, a structuring module 404 and a key field extraction module 406, wherein:

[0093] The text extraction module 402 is used to collect business product information of a preset business field, perform text extraction on the business product information, and obtain unstructured text data.

[0094] The structuring module 404 is used to perform structuring processing on the unstructured text data to obtain structured data.

[0095] The key field extraction module 406 is used to extract key fields from the structured data according to a preset parsing strategy to obtain parsed structured data.

[0096] In an exemplary embodiment, the business product information corresponds to multiple file types, and the multiple file types include a first file type; the text extraction module 402 is also used to convert the business product information of the first file type into an image when the business product information of the first file type is in a preset format; and perform character recognition on the image to obtain unstructured text data.

[0097] In an exemplary embodiment, the text extraction module 402 is further configured to extract text data from the business product information of the first file type to obtain unstructured text data when the business product information of the first file type is not in a preset format.

[0098] In an exemplary embodiment, the business product information corresponds to multiple file types, and the multiple file types include a second file type; the text extraction module 402 is further used to perform character recognition on the business product information of the second file type to obtain unstructured text data.

[0099] In an exemplary embodiment, the structuring module 404 is also used to perform word segmentation processing on unstructured text data through a natural language model to obtain product word segmentations; perform part-of-speech tagging on the product word segmentations through a natural language model to obtain tagged product word segmentations; perform syntactic analysis on the tagged product word segmentations through a natural language model to obtain structured data.

[0100] In an exemplary embodiment, the above device further includes:

[0101] The compensation analysis module is used to determine the incomplete data segment when the parsed structured data is incomplete; match the incomplete data segment with the business product information library corresponding to the preset business field to determine the compensation data; and compensate the incomplete data segment according to the compensation data.

[0102] In an exemplary embodiment, the above device further includes:

[0103] The display module is used to visualize the parsed structured data and business product information.

[0104] Each module in the above-mentioned business product information analysis device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0105] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a business product information parsing method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0106] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0107] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0108] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0109] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0110] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0112] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0113] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for analyzing business product information, characterized in that: The method comprises: Collecting business product information of a preset business field, performing text extraction on the business product information, and obtaining unstructured text data; Performing structured processing on the unstructured text data to obtain structured data; Key fields are extracted from the structured data according to a preset parsing strategy to obtain parsed structured data.

2. The method according to claim 1, characterized in that The business product information corresponds to multiple file types, and the multiple file types include a first file type; The extracting text from the business product information to obtain unstructured text data includes: In a case where the business product information of the first file type is in a preset format, converting the business product information of the first file type into an image; Character recognition is performed on the image to obtain unstructured text data.

3. The method according to claim 2, characterized in that The method further comprises: When the business product information of the first file type is not in a preset format, text data in the business product information of the first file type is extracted to obtain unstructured text data.

4. The method according to claim 1, characterized in that The business product information corresponds to multiple file types, and the multiple file types include a second file type; The extracting text from the business product information to obtain unstructured text data includes: Character recognition is performed on the business product information of the second file type to obtain unstructured text data.

5. The method according to claim 1, characterized in that The step of performing structural processing on the unstructured text data to obtain structured data includes: Performing word segmentation processing on the unstructured text data through a natural language model to obtain product word segmentation; Performing part-of-speech tagging on the product segmentation using the natural language model to obtain the tagged product segmentation; The natural language model is used to perform syntactic analysis on the annotated product segmentation to obtain structured data.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: In the case where the parsed structured data is incomplete, determining an incomplete data segment; Matching the incomplete data segment with the business product information library corresponding to the preset business field to determine compensation data; The incomplete data segment is compensated according to the compensation data.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: The parsed structured data and the business product information are visually displayed.

8. A business product information analysis device, characterized in that: The device comprises: A text extraction module is used to collect business product information of a preset business field, perform text extraction on the business product information, and obtain unstructured text data; A structuring module, used for performing structural processing on the unstructured text data to obtain structured data; The key field extraction module is used to extract key fields from the structured data according to a preset parsing strategy to obtain parsed structured data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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