A method and system for mining Ethereum address entity information

Abstract

The present invention provides an Ethereum address entity information mining method and system, the method comprising the following steps: sorting the Ethereum addresses in an Ethereum address library based on the number of comments on the Ethereum addresses, and reading the comments in the Ethereum addresses one by one based on the position of the Ethereum addresses in the sorting; extracting the entity names in the comments through a preset first neural network model; extracting the place of origin names in the comments through a preset second neural network model; each entity tag is provided with a tag set, each tag in the tag set corresponds to a mapping value, matching words in the comments with each tag in each tag set, obtaining the mapping value of the words in the comments for each tag set, and determining whether the words in the comments are entity tags based on the mapping value corresponding to the words in the comments; segmenting the comments, constructing a word set based on part of speech, and determining the entity behavior terms in the word set based on the word frequency of the words in the word set.

Description

Technical Field

[0001] The present invention relates to the field of data mining technology, and in particular to a method and system for mining Ethereum address entity information. Background Art

[0002] As a window into blockchain technology, the Ethereum Explorer provides users with a convenient gateway to in-depth exploration of the Ethereum ecosystem. This tool integrates a rich set of features, making it easy for even blockchain novices to get started and gain a deeper understanding of the Ethereum blockchain. The Ethereum Explorer allows users to instantly query their accounts. Simply enter an Ethereum address to track all transactions associated with that address, greatly facilitating asset management and transaction verification.

[0003] The Ethereum Explorer also offers a variety of services, including block information query, network status monitoring, and activity logging, fully meeting users' diverse needs for blockchain information. Its user-friendly interface and ease of use make it easy for both technical experts and casual users to quickly master the tool.

[0004] However, for each Ethereum address, its entity information is often difficult to obtain directly through the Ethereum browser. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides an Ethereum address entity information mining method to eliminate or improve one or more defects in the prior art.

[0006] One aspect of the present invention provides a method for mining Ethereum address entity information, the method comprising the steps of:

[0007] Determine the number of comments of an Ethereum address in a pre-built Ethereum address library through an Ethereum browser, sort the Ethereum addresses in the Ethereum address library based on the number of comments of the Ethereum addresses, and read the comments in the Ethereum addresses one by one based on the position of the Ethereum addresses in the sort;

[0008] Extracting entity names from comments using a preset first neural network model to complete entity name extraction;

[0009] Extracting the place of origin name in the comments through a preset second neural network model to complete the extraction of the place of origin name;

[0010] Each entity tag is assigned a tag set, each tag in the tag set corresponds to a mapping value, and the word in the review is matched with each tag in each tag set to obtain the mapping value of the word in the review for each tag set. Based on the mapping value corresponding to the word in the review, it is determined whether the word is an entity tag, thereby completing the extraction of the entity tag;

[0011] The comments are segmented and a word set is constructed based on the part of speech. The entity behavior terms in the word set are determined based on the word frequency of the words in the word set to complete the extraction of entity behavior terms.

[0012] Adopting the above scheme, this scheme first determines the priority extraction of the Ethereum address of the comments based on the number of comments of the Ethereum address, and completes the extraction of comments for the corresponding Ethereum address, and further extracts the content in the comments, extracts the entity name and place of origin name through the neural network model; matches the words of the comment with the preset tag set, determines the mapping value corresponding to the word, determines whether the word is an entity tag based on the mapping value, and completes the extraction of the entity tag; after word segmentation, the word frequency determines the entity behavior term in the word set, and completes the extraction of the entity behavior term; this scheme determines the entity information by deeply mining the comments of the Ethereum address.

[0013] In some embodiments of the present invention, in the steps of sorting the Ethereum addresses in the Ethereum address library based on the number of comments on the Ethereum addresses, and reading the comments in the Ethereum addresses one by one based on the position of the Ethereum addresses in the sort, the Ethereum addresses are placed on the X-axis of the coordinate system based on the sort of the Ethereum addresses in the Ethereum address library, the number of comments on the Ethereum addresses is used as the X-axis of the coordinate system, an image of the coordinate system is constructed, the first Ethereum address on the X-axis whose number of comments is less than a first threshold is determined, and the reading of comments is stopped at this Ethereum address.

[0014] With the above solution, when the number of comments is too small, it means that the Ethereum address has no practical significance. This solution determines the Ethereum address that will not be read for comments based on the number of comments on the Ethereum address, thereby improving reading efficiency.

[0015] In some embodiments of the present invention, after the step of reading the comments in the Ethereum addresses one by one based on the positions of the Ethereum addresses in the sorting, the step further includes: unifying the language of the comments into English.

[0016] In some embodiments of the present invention, in the step of extracting entity names from comments through a preset first neural network model to complete the extraction of entity names, multiple entity names extracted from the same comment are obtained, the position interval of each entity name is determined in the comment, and the entity names with overlapping position intervals are merged.

[0017] In some embodiments of the present invention, in the steps of extracting entity names from comments by using a preset first neural network model to complete the extraction of entity names and extracting place names from comments by using a preset second neural network model to complete the extraction of place names, denoising is performed by using the LLAMA3 large model to delete the noisy entity names and place names.

[0018] In some embodiments of the present invention, in the step of matching a word in a comment with each tag in each tag set to obtain a mapping value of the word in the comment for each tag set, the maximum mapping value of the word in the comment among all tags in a tag set is used as the mapping value of the word.

[0019] In some embodiments of the present invention, in the step of determining whether a word in a comment is an entity tag based on a mapping value corresponding to the word, and completing the extraction of the entity tag, the mapping value of the word is compared with a preset mapping threshold to determine whether the word is an entity tag.

[0020] In some embodiments of the present invention, in the steps of segmenting comments, constructing a word set based on parts of speech, determining entity behavior terms in the word set based on the word frequency of the words in the word set, and completing the extraction of entity behavior terms, words in the word set that are greater than a preset word frequency threshold are used as entity behavior terms.

[0021] In the steps of segmenting the comments, constructing a word set based on parts of speech, determining the entity behavior terms in the word set based on the word frequency of the words in the word set, and completing the extraction of the entity behavior terms, the verbs and nouns in the comments are extracted to construct a word set.

[0022] The second aspect of the present invention also provides an Ethereum address entity information mining system, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0023] The third aspect of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps implemented by the aforementioned Ethereum address entity information mining method.

[0024] Additional advantages, objects, and features of the present invention will be described in part in the following description and will become apparent to those skilled in the art after studying the following or may be learned by practice of the present invention. The objects and other advantages of the present invention may be particularly pointed out and attained in the description and drawings.

[0025] Those skilled in the art will understand that the purposes and advantages that can be achieved by the present invention are not limited to the above specific descriptions, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention.

[0027] Figure 1 Schematic diagram of the first embodiment of the method for mining entity information of Ethereum addresses of the present invention;

[0028] Figure 2 This is a schematic diagram of the second embodiment of the Ethereum address entity information mining method of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0030] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show structures and / or processing steps closely related to the solutions according to the present invention, while other details that are not closely related to the present invention are omitted.

[0031] like Figure 1 As shown, the present invention proposes a method for mining Ethereum address entity information, the method comprising the steps of:

[0032] Step S100, determining the number of comments of an Ethereum address in a pre-built Ethereum address library through an Ethereum browser, sorting the Ethereum addresses in the Ethereum address library based on the number of comments of the Ethereum addresses, and reading the comments in the Ethereum addresses one by one based on the position of the Ethereum addresses in the sort;

[0033] During the specific implementation process, in many Ethereum browsers, users can comment on an Ethereum address. This solution uses some information in the comments to infer the possible entity information of the Ethereum address.

[0034] In a specific implementation process, in the step of sorting the Ethereum addresses in the Ethereum address library based on the number of comments on the Ethereum addresses, the Ethereum addresses in the Ethereum address library are sorted in descending order.

[0035] In the specific implementation process, the Ethereum address library is a pre-stored collection of a large number of Ethereum addresses.

[0036] In its implementation, this solution first builds a local Ethereum client and synchronizes all blocks. Next, it extracts all Ethereum addresses from the blocks, creating a database of Ethereum addresses. However, this database is extremely large. Using an address from the database to query an Ethereum browser for comments about that address would cause the browser to crash. Therefore, this solution relies on the principle that the likelihood of an address containing comments is positively correlated with its turnover: the higher the turnover, the more important the address and the higher the likelihood of comments, and vice versa. Therefore, when querying comments on various Ethereum browsers, this solution prioritizes high-turnover addresses over low-turnover addresses. Furthermore, this solution incorporates a metric called "Comments per 1,000 Addresses," representing the number of comments found for every 1,000 comment queries. Based on this assumption, if the number of addresses queried is on the x-axis and this metric is on the y-axis, the graph should resemble an inversely proportional function. This solution generates this graph in real time as it queries various Ethereum browsers. When this solution notices that the image tends to a number infinitely close to 0 and remains stable for a period of time, we consider the query to be over to avoid any further waste of resources.

[0037] Step S210: extracting entity names from the comments using a preset first neural network model to complete the extraction of entity names;

[0038] Step S220, extracting the place name from the comment using a preset second neural network model, thereby completing the extraction of the place name;

[0039] In the specific implementation process, the first neural network model and can adopt a name database, a spacy library model or a LLAMA3 large model.

[0040] In practice, place names differ significantly from entity names. This is because databases for entity names are often difficult to obtain in their entirety, whereas databases for place names are relatively easy to obtain. As a result, place names are generally much less common than personal names. Therefore, this solution can cover most locations with just the place name database, eliminating the need for AI algorithms to infer place names.

[0041] In step S230, each entity tag is assigned a tag set, each tag in the tag set corresponds to a mapping value, and the word in the review is matched with each tag in each tag set to obtain the mapping value of the word in the review for each tag set. Based on the mapping value corresponding to the word in the review, it is determined whether the word is an entity tag, thereby completing the extraction of the entity tag.

[0042] In the specific implementation process, since each entity tag often has multiple names, and multiple names correspond to one entity tag, this solution constructs multiple names of an entity tag as tags into a tag set, and sets different mapping values ​​based on the correlation between each tag and the entity tag to reflect the degree of relationship between each tag and the entity tag, and determines whether the word in the comment is an entity tag based on the mapping value corresponding to the word.

[0043] In the specific implementation process, when a word in a comment corresponds to multiple tags in a tag set, the word has multiple mapping values ​​corresponding to multiple tags. The maximum value is taken as the mapping value corresponding to the word. The mapping value of the word is compared with the preset mapping judgment threshold. If the mapping value of the word is greater than the preset mapping judgment threshold, the word is judged as an entity tag.

[0044] Step S240 , segmenting the comments, building a word set based on parts of speech, determining entity behavior terms in the word set based on the word frequencies of the words in the word set, and completing the extraction of entity behavior terms.

[0045] In the specific implementation process, in the step of segmenting the comments, the spacy library model segments the comments.

[0046] In the specific implementation process, dictionary matching, morphological rules or LLAMA3 model are used to identify parts of speech;

[0047] Dictionary matching: This method matches words in the text with entries in a predefined dictionary, determining the word's part of speech based on the word's part-of-speech tagging information in the dictionary. This method relies on the completeness and accuracy of the dictionary and may not be effective for words not included in the dictionary or for words with multiple meanings.

[0048] Morphological rules: Use the morphological features of words (such as prefixes, suffixes, and roots) to infer parts of speech; for example, in English, the suffix "-ly" usually indicates an adverb, while the suffix "-ness" often indicates a noun.

[0049] Adopting the above scheme, this scheme first determines the priority extraction of the Ethereum address of the comments based on the number of comments of the Ethereum address, and completes the extraction of comments for the corresponding Ethereum address, and further extracts the content in the comments, extracts the entity name and place of origin name through the neural network model; matches the words of the comment with the preset tag set, determines the mapping value corresponding to the word, determines whether the word is an entity tag based on the mapping value, and completes the extraction of the entity tag; after word segmentation, the word frequency determines the entity behavior term in the word set, and completes the extraction of the entity behavior term; this scheme determines the entity information by deeply mining the comments of the Ethereum address.

[0050] In some embodiments of the present invention, in the steps of sorting the Ethereum addresses in the Ethereum address library based on the number of comments on the Ethereum addresses, and reading the comments in the Ethereum addresses one by one based on the position of the Ethereum addresses in the sort, the Ethereum addresses are placed on the X-axis of the coordinate system based on the sort of the Ethereum addresses in the Ethereum address library, the number of comments on the Ethereum addresses is used as the X-axis of the coordinate system, an image of the coordinate system is constructed, the first Ethereum address on the X-axis whose number of comments is less than a first threshold is determined, and the reading of comments is stopped at this Ethereum address.

[0051] In the specific implementation process, the Ethereum addresses in the Ethereum address database are sorted in descending order, and the number of comments on the Ethereum addresses gradually decreases in the extension direction of the X-axis of the coordinate system.

[0052] With the above solution, when the number of comments is too small, it means that the Ethereum address has no practical significance. This solution determines the Ethereum address that will not be read for comments based on the number of comments on the Ethereum address, thereby improving reading efficiency.

[0053] In some embodiments of the present invention, after the step of reading the comments in the Ethereum addresses one by one based on the positions of the Ethereum addresses in the sorting, the step further includes: unifying the language of the comments into English.

[0054] During implementation, when standardizing the language of comments to English, after extracting comments from various Ethereum addresses, this solution will unify all comments to English to facilitate subsequent data processing. This solution utilizes online translation for this purpose. This solution reduces the number of requests by merging translations of similar languages. Specifically, it first iterates through all comments and categorizes them by language using the langid library. For comments in the same language, this solution concatenates them into a single large string using delimited strings before translating them. Assuming this solution currently has three sentences, this solution concatenates them using delimited strings before translating them. Because the delimited strings contain special characters, the translator will not translate them; instead, it will only translate them. This solution simply segments the translated string to obtain the corresponding translation.

[0055] Generally speaking, online translation supports a maximum of 5,000 characters. Therefore, this method allows you to concatenate strings to a length of no more than 5,000 characters, significantly improving translation efficiency. Furthermore, you can use a pipelined approach to process data, allowing simultaneous query and translation of review data without having to wait for all data to be queried before starting translation.

[0056] Specifically, we currently have Three sentences. This solution uses delimited strings Concatenate the three sentences into Then translate it. consists of special characters, the translator will not translate them, but only This solution only requires the translated strings to be translated according to Split, and you get The corresponding translation content.

[0057] In some embodiments of the present invention, in the step of extracting entity names from comments through a preset first neural network model to complete the extraction of entity names, multiple entity names extracted from the same comment are obtained, the position interval of each entity name is determined in the comment, and the entity names with overlapping position intervals are merged.

[0058] In a specific implementation, the entity label refers to a word that describes which organization the address belongs to.

[0059] In the specific implementation process, if "zhang han sheng" exists in the comment, the extracted entity names include "zhang han" and "han sheng". If "zhang han sheng" does not exist in the entity name database, but "hansheng" and "zhang han" exist, the entity names need to be merged;

[0060] This solution uses name interval stacking. This solution takes the index intervals of all names in the comment. For example, if the comment is "This address belongs to zhang han sheng," the position interval for "zhang han" is [24, 32], and the position interval for "han sheng" is [30, 38]. If the two position intervals overlap, they are merged to [24, 38], resulting in the merged entity name "zhang han sheng."

[0061] By adopting the above scheme, the present scheme can merge entity names according to their positions in the comments, which can reduce the number of entity names to be extracted on the one hand, and improve the accuracy of the entity names finally extracted on the other hand.

[0062] like Figure 2As shown, in some embodiments of the present invention, after determining whether the word is an entity tag based on the mapping value corresponding to the word in the comment and completing the step of extracting the entity tag, step S231 is also included to determine the position in the comment where the extracted entity name is located, and whether there is a noise word in the preset noise word set within a threshold distance range before and after the comment position. If so, the entity tag is deleted.

[0063] The threshold distance may be 4 words.

[0064] In practice, after extracting entity names, this solution no longer uses LLAMA3 to remove noise. Instead, it removes noise based on known patterns. Because entity tag features are relatively typical and only appear in specific semantic contexts, this approach can effectively improve noise filtering speed. Based on the characteristics of the data, this solution constructs a noise exclusion database. These words generally appear within four words before and after the tag matching position. Therefore, for each matching position, if a word hits the noise exclusion database, we exclude the match and remove some entity tags that are not relevant.

[0065] In some embodiments of the present invention, in the steps of extracting entity names from comments by using a preset first neural network model to complete the extraction of entity names and extracting place names from comments by using a preset second neural network model to complete the extraction of place names, denoising is performed by using the LLAMA3 large model to delete the noisy entity names and place names.

[0066] In some embodiments of the present invention, in the step of matching a word in a comment with each tag in each tag set to obtain a mapping value of the word in the comment for each tag set, the maximum mapping value of the word in the comment among all tags in a tag set is used as the mapping value of the word.

[0067] In some embodiments of the present invention, in the step of determining whether a word in a comment is an entity tag based on a mapping value corresponding to the word, and completing the extraction of the entity tag, the mapping value of the word is compared with a preset mapping threshold to determine whether the word is an entity tag.

[0068] In the specific implementation process, if an entity tag is coin base Exchange, the tag set corresponding to the entity tag includes fishcoin, coinbase and Exchange. The mapping value corresponding to fishcoin is 100, the mapping value corresponding to coinbase is 90, and the mapping value corresponding to Exchange is 10. If a comment includes coinbase and Exchange, the corresponding mapping values ​​are 90 and 10. The maximum mapping value is 90, and 90 is compared with the preset mapping threshold. If 90 is greater than the preset mapping threshold, coinbase is determined to be the entity tag; if a comment only includes Exchange, the corresponding mapping value is 10, the maximum mapping value is 10, and 10 is compared with the preset mapping threshold. If 10 is not greater than the preset mapping threshold, there is no entity tag in the comment.

[0069] By adopting the above scheme, the present scheme can determine the entity label of a commenting employee through the mapping value through the preset label set corresponding to the entity label, thereby improving the extraction accuracy.

[0070] In some embodiments of the present invention, in the steps of segmenting comments, constructing a word set based on parts of speech, determining entity behavior terms in the word set based on the word frequency of the words in the word set, and completing the extraction of entity behavior terms, words in the word set that are greater than a preset word frequency threshold are used as entity behavior terms.

[0071] In the steps of segmenting the comments, constructing a word set based on parts of speech, determining the entity behavior terms in the word set based on the word frequency of the words in the word set, and completing the extraction of the entity behavior terms, the verbs and nouns in the comments are extracted to construct a word set.

[0072] An embodiment of the present invention also provides an Ethereum address entity information mining system, which includes a computer device, wherein the computer device includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0073] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the steps of the aforementioned method for mining entity information of Ethereum addresses. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, floppy disk, hard disk, removable storage disk, CD-ROM, or any other form of storage medium known in the art.

[0074] It should be understood by those skilled in the art that the various exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether to implement the system in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention. When implemented in hardware, it may be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via a data signal carried in a carrier wave.

[0075] It should be understood that the present invention is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method of the present invention is not limited to the specific steps described and illustrated. Those skilled in the art may make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present invention.

[0076] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or replace features of other embodiments.

[0077] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations to the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for mining Ethereum address entity information, characterized in that: The steps of the method include: Determine the number of comments of an Ethereum address in a pre-built Ethereum address library through an Ethereum browser, sort the Ethereum addresses in the Ethereum address library based on the number of comments of the Ethereum addresses, and read the comments in the Ethereum addresses one by one based on the position of the Ethereum addresses in the sort; Extracting entity names from comments using a preset first neural network model to complete entity name extraction; Extracting the place of origin name in the comments through a preset second neural network model to complete the extraction of the place of origin name; Each entity tag is correspondingly provided with a tag set, and each tag in the tag set corresponds to a mapping value. The words in the review are matched with each tag in each tag set to obtain the mapping value of the word in the review for each tag set. Based on the mapping value corresponding to the word in the review, it is determined whether the word is an entity tag, and the entity tag is extracted. The position of the extracted entity name in the review is determined, and whether there is a noise word in the preset noise word set within a threshold distance range before and after the position in the review. If so, the entity tag is deleted; The comments are segmented and a word set is constructed based on the part of speech. The entity behavior terms in the word set are determined based on the word frequency of the words in the word set to complete the extraction of entity behavior terms.

2. The method for mining Ethereum address entity information according to claim 1, characterized in that: In the steps of sorting the Ethereum addresses in the Ethereum address library based on the number of comments on the Ethereum addresses, and reading the comments in the Ethereum addresses one by one based on the positions of the Ethereum addresses in the sort, the Ethereum addresses are placed on the X-axis of the coordinate system based on the sort of the Ethereum addresses in the Ethereum address library, the number of comments on the Ethereum addresses is used as the X-axis of the coordinate system, an image of the coordinate system is constructed, the first Ethereum address on the X-axis whose number of comments is less than a first threshold is determined, and the reading of comments is stopped at this Ethereum address.

3. The method for mining Ethereum address entity information according to claim 1, wherein: After the step of reading the comments in the Ethereum addresses one by one based on the positions of the Ethereum addresses in the sorting, the method further includes: unifying the language of the comments into English.

4. The method for mining Ethereum address entity information according to any one of claims 1 to 3, characterized in that: In the step of extracting entity names from comments through a preset first neural network model to complete the extraction of entity names, multiple entity names extracted from the same comment are obtained, the position interval of each entity name is determined in the comment, and entity names with overlapping position intervals are merged.

5. The method for mining Ethereum address entity information according to claim 1, wherein: In the steps of extracting entity names from comments by using the preset first neural network model to complete the extraction of entity names and extracting place names from comments by using the preset second neural network model to complete the extraction of place names, denoising is performed by using the LLAMA3 large model to delete the noisy entity names and place names.

6. The method for mining Ethereum address entity information according to claim 1, wherein: In the step of matching the words in the comment with each tag in each tag set to obtain the mapping value of the words in the comment for each tag set, the maximum mapping value of the word in the comment among all tags in a tag set is used as the mapping value of the word.

7. The method for mining Ethereum address entity information according to claim 1 or 6, wherein: In the step of determining whether a word in a comment is an entity tag based on a mapping value corresponding to the word, and completing the extraction of the entity tag, the mapping value of the word is compared with a preset mapping threshold to determine whether the word is an entity tag.

8. The method for mining Ethereum address entity information according to claim 1, wherein: In the steps of segmenting comments, constructing a word set based on parts of speech, determining entity behavior terms in the word set based on the word frequency of the words in the word set, and completing the extraction of entity behavior terms, the words in the word set that are greater than a preset word frequency threshold are used as entity behavior terms.

9. An Ethereum address entity information mining system, characterized in that: The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions. The processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method according to any one of claims 1 to 8.

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