Multi-language adaptive switching method, system, equipment and medium

Abstract

The invention discloses a multi-language adaptive switching method, system and device and a medium, and the method comprises the steps: obtaining request information sent by a user terminal, and analyzing a language priority list contained in the request information, so as to recognize multiple languages and corresponding priority ranks; obtaining current Web application type information and historical access data of the user, and judging the language preference currently required by the user according to a language priority matching algorithm in combination with the language priority list to obtain a language decision result; and matching a corresponding language version based on the language decision result, loading the language version and switching the original voice content into a specified language so as to realize multi-language adaptive switching. In order to solve the problems of inaccurate recognition, poor experience and insufficient flexibility caused by the fact that existing Web multi-language switching depends on single dimension, preference is intelligently judged and versions are switched through algorithm weights, the language switching accuracy and user experience are remarkably improved, and the method is suitable for different scenes.

Description

Technical Field

[0001] The present application relates to the field of computer software and Internet application technology, and in particular to a multi-language adaptive switching method, system, device, and medium. Background Art

[0002] With the globalization of the Internet, the demand for multilingual adaptive switching on websites is becoming increasingly urgent. However, existing technologies have significant drawbacks:

[0003] 1. Determining language preference based on IP address: Inferring language preference based solely on a user's IP address cannot reflect the user's actual language and cultural background or personal habits. For example, a Chinese user may prefer to browse technical documentation in English, or a user's use of a VPN may lead to misjudgment of their geographic location, resulting in low language version matching accuracy.

[0004] 2. Manual language selection by the user: This approach relies on users to actively operate the language switching control, increasing interaction costs. This can lead to user loss due to language barriers, especially during first-time visits. Furthermore, seamless adaptive switching cannot be achieved, resulting in a poor user experience.

[0005] Existing solutions generally lack refined processing of language priority, intelligent inference of user intent, and flexible and configurable policy architecture, resulting in the accuracy, timeliness, and user experience of multi-language switching being unable to meet the needs of global web applications.

[0006] In summary, the problems existing in the prior art need to be solved urgently. Summary of the Invention

[0007] This application proposes a multi-language adaptive switching method, system, device, and medium to address the problems of inaccurate recognition, poor experience, and insufficient flexibility caused by the existing Web multi-language switching relying on a single dimension. By intelligently judging preferences and switching versions through algorithmic weights, the method significantly improves language switching accuracy and user experience, and adapts to different scenarios.

[0008] The present invention provides a multi-language adaptive switching method, comprising:

[0009] Obtaining request information sent by the user terminal, parsing the language priority list contained in the request information to identify multiple languages ​​and their corresponding priority rankings;

[0010] Obtaining the current web application type information and the user's historical access data, combining the language priority list, and determining the user's current language preference according to the language priority matching algorithm to obtain a language decision result;

[0011] The corresponding language version is matched based on the language decision result, the language version is loaded and the original voice content is switched to the specified language to achieve multi-language adaptive switching.

[0012] According to a multi-language adaptive switching method provided by the present invention, before determining the user's current language preference according to the language priority matching algorithm, the method further includes:

[0013] Analyze language weights and dynamically adjust weights based on real-time user feedback. The language weights are used to measure the importance of each language in the user's language preference.

[0014] According to a multi-language adaptive switching method provided by the present invention, the language decision result matches the corresponding language version, including:

[0015] Calculating a comprehensive score for each candidate language from the list of languages ​​supported by the current website application through weighted calculation and rule-based judgment, where the comprehensive score is based on the language's priority, weight, and match with the website's data and user data characteristics, and selecting the language with the highest comprehensive score and supported by the website as the best matching language;

[0016] If the match is successful, the original default language is switched to the best matching language; if the match fails, the default language is maintained.

[0017] According to a multi-language adaptive switching method provided by the present invention, after loading the matched language version and switching the network application content to the specified language, the method further includes:

[0018] A multi-level cache acceleration mechanism is adopted to store the language decision results in an index database.

[0019] The present invention also provides a multi-language adaptive switching system, the system comprising:

[0020] an identification module, configured to obtain a request message sent by a user terminal and parse the language priority list contained in the request message to identify multiple languages ​​and their corresponding priority rankings;

[0021] A matching module is used to obtain the current web application type information and the user's historical access data, combine it with the language priority list, and determine the user's current language preference according to the language priority matching algorithm to obtain a language decision result;

[0022] An execution module is used to match a corresponding language version based on the language decision result, load the language version and switch the original voice content to a specified language to achieve multi-language adaptive switching.

[0023] According to a multi-language adaptive switching system provided by the present invention, before determining the user's current language preference according to the language priority matching algorithm, the system further includes:

[0024] The analysis module is used to analyze language weights and dynamically adjust the weights based on real-time user feedback. The language weights are used to measure the importance of each language in the user's language preference.

[0025] According to a multi-language adaptive switching system provided by the present invention, the language decision result matches the corresponding language version, including:

[0026] Calculating a comprehensive score for each candidate language from the list of languages ​​supported by the current website application through weighted calculation and rule-based judgment, where the comprehensive score is based on the language's priority, weight, and match with the website's data and user data characteristics, and selecting the language with the highest comprehensive score and supported by the website as the best matching language;

[0027] If the match is successful, the original default language is switched to the best matching language; if the match fails, the default language is maintained.

[0028] According to the present invention, a multi-language adaptive switching system, after loading the matched language version and switching the network application content to the specified language, further includes:

[0029] A multi-level cache acceleration mechanism is adopted to store the language decision results in an index database.

[0030] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a multi-language adaptive switching method as described above is implemented.

[0031] The present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the multi-language adaptive switching method described above is implemented.

[0032] The present invention provides a multi-language adaptive switching method, system, device and medium, which analyzes the browser language priority list, combines the Web application type and the user's historical access data, and uses the language priority matching algorithm to intelligently judge the user's language preference and switch to the corresponding language version. The present invention breaks through the limitation of the existing technology that relies on a single language judgment dimension, realizes the accurate identification of the user's real language needs, can significantly improve the accuracy of multi-language switching and user experience, and reduce the user's manual operation costs. By integrating data from multiple different sources, it effectively solves the problems of high misjudgment rate of traditional IP positioning and disconnection between browser language settings and actual needs. It is particularly suitable for differentiated language scenarios such as technical documents and cross-border e-commerce, and provides a more intelligent and flexible solution for the international adaptation of Web applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0034] Figure 1 This is a flow chart of a multi-language adaptive switching method provided by the present invention;

[0035] Figure 2 This is a structural diagram of a multi-language adaptive switching system provided by this application;

[0036] Figure 3 It is a structural schematic diagram of the electronic equipment provided by the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0038] With the globalization of the Internet, the demand for multilingual adaptive switching on websites is becoming increasingly urgent. However, existing technologies have significant drawbacks:

[0039] 1. Determining language preference based on IP address: Inferring language preference based solely on a user's IP address cannot reflect the user's actual language and cultural background or personal habits. For example, a Chinese user may prefer to browse technical documentation in English, or a user's use of a VPN may lead to misjudgment of their geographic location, resulting in low language version matching accuracy.

[0040] 2. Manual language selection by the user: This approach relies on users to actively operate the language switching control, increasing interaction costs. This can lead to user loss due to language barriers, especially during first-time visits. Furthermore, seamless adaptive switching cannot be achieved, resulting in a poor user experience.

[0041] Existing solutions generally lack refined processing of language priority, intelligent inference of user intent, and flexible and configurable policy architecture, resulting in the accuracy, timeliness, and user experience of multi-language switching being unable to meet the needs of global web applications.

[0042] In order to solve the problems in the existing technology, the present invention proposes a multi-language adaptive switching method to solve the problems of inaccurate recognition, poor experience and insufficient flexibility caused by the existing Web multi-language switching relying on a single dimension. By intelligently judging preferences and switching versions through algorithm weights, the accuracy of language switching and user experience are significantly improved, and it is suitable for different scenarios, such as Figure 1 As shown, it includes steps 101 to 103, and each step is specifically as follows:

[0043] Step 101: Obtain a request message sent by a user terminal, and parse the language priority list contained in the request message to identify multiple languages ​​and their corresponding priority rankings;

[0044] As an example of this embodiment, all supported language codes are explicitly listed in the website backend or configuration file, such as ['zh-CN', 'en', 'fr']. The configured language code is read when the system starts or when needed. When a user accesses a web application, the system captures HTTP request information through the front-end or edge node, extracts the Accept-Language field contained in the request header and the language priority list set by the user in the past. The system parses this field to identify multiple languages ​​and their priority rankings, converts language tags and priority weights into structured data, provides basic data for subsequent language preference judgment, and realizes the preliminary identification and quantification of user language preferences.

[0045] Step 102: Obtain the current web application type information and the user's historical access data, combine them with the language priority list, and determine the user's current language preference according to the language priority matching algorithm to obtain a language decision result;

[0046] As an example of this embodiment, the present invention uses multi-dimensional information fusion as the core logic, and implements intelligent inference of the user's actual language preference through switching strategies and priority rules defined in JSON or YAML configuration files:

[0047] First, multi-factor weighting is configured. Factors such as browser language priority, web application type matching, user history, IP location (for fallback assistance), and user manual selection (the highest persistence weight) are assigned weights ranging from 0 to 1. For example, for a technical documentation website, the English application type matching weight can be set to 1.3, and the Chinese application type to 1.0, so that the system prioritizes scenario relevance when making decisions.

[0048] Secondly, application-type-specific rules override the default logic for different scenarios. For example, on a technical website, if the browser supports English, even if Chinese ranks higher in the priority list, the rules will force English to be preferred, addressing the differences in requirements between general scenarios and vertical fields.

[0049] The second is user behavior-driven rules. By using embedded data to collect statistics on user language switching frequency, stay time, and other behaviors, the implicit priority is dynamically adjusted. For example, if a user manually switches to English three times, the system will automatically increase the English weight by 0.2 and the weight will remain in effect for 24 hours.

[0050] Next is the fallback strategy. When language matching fails or information is missing, it falls back step by step in the preset order (e.g., website default → English → IP inferred language) to avoid display anomalies caused by no matching results.

[0051] Finally, there is the manual selection of persistence rules, which allows configuration of manually selected coverage (such as current session only or memory for one year) and weight, ensuring that users' actively selected permissions always take precedence over automatic inference, while balancing personalized needs and system intelligence through validity period settings.

[0052] The above dynamic management is achieved through configuration files. The language decision result is generated through weighted calculation and rule judgment based on the acquired Web application type, user history data and language priority list.

[0053] Step 103 : matching a corresponding language version based on the language decision result, loading the language version and switching the original voice content to a specified language to achieve multi-language adaptive switching.

[0054] The present invention provides a multi-language adaptive switching method, which analyzes the browser language priority list, combines the Web application type and the user's historical access data, and uses the language priority matching algorithm to intelligently judge the user's language preference and switch to the corresponding language version. The present invention breaks through the limitation of the existing technology that relies on a single language judgment dimension, realizes the accurate identification of the user's real language needs, can significantly improve the accuracy of multi-language switching and user experience, and reduce the user's manual operation costs. By integrating data from multiple different sources, it effectively solves the problems of high misjudgment rate of traditional IP positioning and disconnection between browser language settings and actual needs. It is particularly suitable for differentiated language scenarios such as technical documents and cross-border e-commerce, and provides a more intelligent and flexible solution for the international adaptation of Web applications.

[0055] As a further optional embodiment, before determining the user's current language preference according to the language priority matching algorithm, the method further includes:

[0056] Analyze language weights and dynamically adjust weights based on real-time user feedback. The language weights are used to measure the importance of each language in the user's language preference.

[0057] In this embodiment, the system dynamically adjusts language weights based on real-time feedback from users to better suit users' changing language preferences.

[0058] Preferably, the control over the weight distribution in the system lies with the developer. In addition to the above-mentioned weight distribution through the configuration file, an interface can also be set up in the website backend management system to set the weights of different factors by entering numerical values ​​or adjusting sliders; or a machine learning model can be used to automatically generate or adjust weights based on a large amount of user data analysis results. Those skilled in the art can flexibly configure according to actual needs, and the present invention is not limited to this.

[0059] As a further optional embodiment, the language decision result matches the corresponding language version, including:

[0060] Calculating a comprehensive score for each candidate language from the list of languages ​​supported by the current website application through weighted calculation and rule-based judgment, where the comprehensive score is based on the language's priority, weight, and match with the website's data and user data characteristics, and selecting the language with the highest comprehensive score and supported by the website as the best matching language;

[0061] If the match is successful, the original default language is switched to the best matching language; if the match fails, the default language is maintained.

[0062] It can be understood that a comprehensive score is calculated for each candidate language based on language priority, scenario matching, behavior matching and corresponding weights. The calculation formula is: Comprehensive score = Language priority score × α + Scenario matching score × β + Behavior matching score × γ.

[0063] Among them, the language priority score is mapped according to the sorting position in the browser list; the scenario matching score comes from the preset rule library; and the behavior matching score is based on the frequency of use in the user's history. After the scoring is completed, the algorithm sorts the candidate languages ​​in descending order according to the comprehensive score, and selects the language with the highest score as the preliminary matching result. If there is a tie, the secondary judgment rule is triggered: first compare the frequency of use of the two languages ​​in the user's historical visits, and give priority to the high-frequency language; if the language usage frequency is the same, follow the original priority order of the browser; finally, select the language with the highest score and supported by the website as the best matching language. If the match is successful, switch the language and cache the result. If it fails, maintain the default language.

[0064] As a further optional embodiment, after loading the matched language version and switching the web application content to the specified language, the method further includes:

[0065] A multi-level cache acceleration mechanism is adopted to store the language decision results in an index database.

[0066] Preferably, the system adopts a browser-edge-center three-level cache acceleration mechanism to achieve efficient storage and coordinated update of language decision results and resources:

[0067] First, the browser stores the language decision results in an IndexedDB database with a 24-hour validity period. This local cache allows the browser to directly retrieve the historical decision results when the same user visits again, eliminating the need for repeated network requests and improving response times to milliseconds.

[0068] Secondly, edge nodes are responsible for caching popular language strategies and resource packages. By analyzing user access frequency, edge nodes store decision rules and resource packages for high-frequency languages ​​in a local cache, achieving a hit rate of over 90%. When a user request reaches an edge node, if the cache matches the target language, the local strategy and resource package can be directly invoked to complete the response without returning to the source central server, reducing network latency to less than 50ms. If the cache does not match the target language, the edge node initiates an on-demand incremental synchronization with the central server, obtaining only the differential data to update the cache, avoiding bandwidth waste caused by full transmission.

[0069] Finally, the central server serves as the data source, storing all language resource packages, policy rules, and user feedback data. When developers update language policies or release new language versions, the central server ensures cache consistency through a push-pull update mechanism:

[0070] Active push: Send a version update notification to the edge node, carrying a policy difference file. After receiving the notification, the edge node marks the old cache invalid and triggers an incremental update.

[0071] Passive pull: When the cache expires (such as setting a validity period of 7 days) or misses, the edge node automatically pulls the latest data from the central server and updates the local cache.

[0072] At the same time, the central server assigns unique version numbers to language resource packages and policy rules, and browsers and edge nodes determine whether the cache is expired by comparing the version numbers.

[0073] Through the cache acceleration mechanism, the system achieves fast local response on the browser side, edge nodes share the pressure of hot spot requests, and the central server centrally manages the data source. The three work together through version control, invalidation notification and incremental synchronization protocol, which not only improves the response efficiency of multi-language switching, but also ensures the real-time consistency of cached data at all levels.

[0074] The following describes a multi-language adaptive switching system provided by the present invention. Figure 2 As shown, the multi-language adaptive switching system described below and the multi-language adaptive switching method described above can refer to each other.

[0075] A multi-language adaptive switching system, comprising:

[0076] an identification module, configured to obtain a request message sent by a user terminal and parse the language priority list contained in the request message to identify multiple languages ​​and their corresponding priority rankings;

[0077] A matching module is used to obtain the current web application type information and the user's historical access data, combine it with the language priority list, and determine the user's current language preference according to the language priority matching algorithm to obtain a language decision result;

[0078] An execution module is used to match a corresponding language version based on the language decision result, load the language version and switch the original voice content to a specified language to achieve multi-language adaptive switching.

[0079] As a further optional embodiment, before determining the user's current language preference according to the language priority matching algorithm, the method further includes:

[0080] The analysis module is used to analyze language weights and dynamically adjust the weights based on real-time user feedback. The language weights are used to measure the importance of each language in the user's language preference.

[0081] According to a multi-language adaptive switching system provided by the present invention, the language decision result matches the corresponding language version, including:

[0082] Calculating a comprehensive score for each candidate language from the list of languages ​​supported by the current website application through weighted calculation and rule-based judgment, where the comprehensive score is based on the language's priority, weight, and match with the website's data and user data characteristics, and selecting the language with the highest comprehensive score and supported by the website as the best matching language;

[0083] If the match is successful, the original default language is switched to the best matching language; if the match fails, the default language is maintained.

[0084] As a further optional embodiment, after loading the matched language version and switching the web application content to the specified language, the method further includes:

[0085] A multi-level cache acceleration mechanism is adopted to store the language decision results in an index database.

[0086] The present invention provides a multi-language adaptive switching system, which analyzes the browser language priority list, combines the Web application type and the user's historical access data, and uses the language priority matching algorithm to intelligently judge the user's language preference and switch to the corresponding language version. The present invention breaks through the limitation of the existing technology that relies on a single language judgment dimension, realizes the accurate identification of the user's real language needs, can significantly improve the accuracy and user experience of multi-language switching, and reduce the user's manual operation costs. By integrating data from multiple different sources, it effectively solves the problems of high misjudgment rate of traditional IP positioning and disconnection between browser language settings and actual needs. It is particularly suitable for differentiated language scenarios such as technical documents and cross-border e-commerce, and provides a more intelligent and flexible solution for the international adaptation of Web applications.

[0087] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute a multi-language adaptive switching method, which includes:

[0088] Obtaining request information sent by the user terminal, parsing the language priority list contained in the request information to identify multiple languages ​​and their corresponding priority rankings;

[0089] Obtaining the current web application type information and the user's historical access data, combining the language priority list, and determining the user's current language preference according to the language priority matching algorithm to obtain a language decision result;

[0090] The corresponding language version is matched based on the language decision result, the language version is loaded and the original voice content is switched to the specified language to achieve multi-language adaptive switching.

[0091] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0092] On the other hand, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program is implemented to perform a multi-language adaptive switching method provided by the above methods, the method comprising:

[0093] Obtaining request information sent by the user terminal, parsing the language priority list contained in the request information to identify multiple languages ​​and their corresponding priority rankings;

[0094] Obtaining the current web application type information and the user's historical access data, combining the language priority list, and determining the user's current language preference according to the language priority matching algorithm to obtain a language decision result;

[0095] The corresponding language version is matched based on the language decision result, the language version is loaded and the original voice content is switched to the specified language to achieve multi-language adaptive switching.

[0096] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0097] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-language adaptive switching method, characterized in that: include: Obtaining request information sent by the user terminal, parsing the language priority list contained in the request information to identify multiple languages ​​and their corresponding priority rankings; Obtaining the current web application type information and the user's historical access data, combining the language priority list, and determining the user's current language preference according to the language priority matching algorithm to obtain a language decision result; The corresponding language version is matched based on the language decision result, the language version is loaded and the original voice content is switched to the specified language to achieve multi-language adaptive switching.

2. A multi-language adaptive switching method according to claim 1, characterized in that: Before determining the user's current language preference according to the language priority matching algorithm, the method further includes: Analyze language weights and dynamically adjust weights based on real-time user feedback. The language weights are used to measure the importance of each language in the user's language preference.

3. The multi-language adaptive switching method according to claim 1, characterized in that: The language decision result matches the corresponding language version, including: Calculating a comprehensive score for each candidate language from the list of languages ​​supported by the current website application through weighted calculation and rule-based judgment, where the comprehensive score is based on the language's priority, weight, and match with the website's data and user data characteristics, and selecting the language with the highest comprehensive score and supported by the website as the best matching language; If the match is successful, the original default language is switched to the best matching language; if the match fails, the default language is maintained.

4. The multi-language adaptive switching method according to claim 1, characterized in that: After loading the matching language version and switching the web application content to the specified language, it also includes: A multi-level cache acceleration mechanism is adopted to store the language decision results in an index database.

5. A multi-language adaptive switching system, characterized in that: include: an identification module, configured to obtain a request message sent by a user terminal and parse the language priority list contained in the request message to identify multiple languages ​​and their corresponding priority rankings; A matching module is used to obtain the current web application type information and the user's historical access data, combine it with the language priority list, and determine the user's current language preference according to the language priority matching algorithm to obtain a language decision result; An execution module is used to match a corresponding language version based on the language decision result, load the language version and switch the original voice content to a specified language to achieve multi-language adaptive switching.

6. A multi-language adaptive switching system according to claim 5, characterized in that: Before determining the user's current language preference according to the language priority matching algorithm, the method further includes: The analysis module is used to analyze language weights and dynamically adjust the weights based on real-time user feedback. The language weights are used to measure the importance of each language in the user's language preference.

7. The multi-language adaptive switching system according to claim 5, characterized in that: The language decision result matches the corresponding language version, including: Calculating a comprehensive score for each candidate language from the list of languages ​​supported by the current website application through weighted calculation and rule-based judgment, where the comprehensive score is based on the language's priority, weight, and match with the website's data and user data characteristics, and selecting the language with the highest comprehensive score and supported by the website as the best matching language; If the match is successful, the original default language is switched to the best matching language; if the match fails, the default language is maintained.

8. The multi-language adaptive switching system according to claim 5, characterized in that: After loading the matching language version and switching the web application content to the specified language, it also includes: A multi-level cache acceleration mechanism is adopted to store the language decision results in an index database.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the multi-language adaptive switching method according to any one of claims 1 to 7 is implemented.

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

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