Imagine you’re the captain of a massive ship navigating treacherous waters. You need a crew that knows how to handle the challenges and make the right decisions at every turn. Similarly, a team of experts faced with the task of translating archive documents needs proper guidance. This manuscript proposes an intelligent recommendation system that can navigate the vast sea of archive translation tasks. By using proper names and terms, this system aims to assign tasks based on intelligent matching principles. Just like different labels help identify the contents of various archives, system users are divided into different professional domains, such as economic knowledge or language skills. With these labels, the intelligent recommendation system can make precise matches between archives and users, leading to improved efficiency and quality in translation tasks. Through multi-label classification, the system can allocate tasks intelligently to users based on their expertise. It’s like having a crew with specialized skills assigned to specific parts of the ship. The construction of an intelligent control model ensures that over time, the performance of task allocation improves without the need for direct input from task issuers. To explore this exciting voyage through archive translation, delve into the full article!

How to recruit, test, and train the intelligent recommendation system users, and how to assign the archive translation tasks to all intelligent recommendation system users according to the intelligent matching principles are still a problem that needs to be solved. With the help of proper names and terms in China’s Imperial Maritime Customs archives, this manuscript aims to solve the problem. When the corresponding translation, domain or attributes of a proper name or term is known, it will be easier for some archive translation tasks to be completed, and the adaptive archive intelligent recommendation system will also improve the efficiency of intelligent recommendation quality of archive translation tasks. These related domains or attributes are different labels of these archives. To put it simply, multi-label classification means that the same instance can have multiple labels or be labelled into multiple categories, which is called multi-label classification. With the multi-label classification, archives can be classified into different categories, such as the trade archives, preventive archives, personnel archives, etc. The system users are divided into different professional domains by some tests, for instance, system users who are good at economic knowledge and users who have higher language skills. With these labels, the intelligent recommendation system can make the intelligent match between the archives and system users, so as to improve the efficiency and quality of intelligent archive translation tasks. In this manuscript, through multi-label classification, the intelligent recommendation system can realize the intelligent allocation of archive translation tasks to the system users. The intelligent allocation is realized through the construction of intelligent control model, and verifies that the intelligent recommendation system can improve the performance of task allocation over time without the participation of task issuers.

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