It is **possible** to speculate on a person's interests through the library's borrowing history, but it is necessary to combine data analysis and reasonable inference, and at the same time pay attention to **Privacy protection**and **data limitations**. The following is the specific analysis： --- ###**First, a feasible method** 1. **Book classification analysis** -Library systems usually encode books by subject (such as literature, history, science and technology, etc.) or by the middle picture classification method. By counting the category distribution of books borrowed by a person, it is possible to speculate on his field of interest (such as his preference for science fiction or economic books). -**Example**: If someone frequently borrows science fiction novels such as "Three Bodies" and "Base", they may be interested in science fiction or technology. 2. **Keyword and topic mining** -Perform natural language processing (NLP) on book titles, abstracts, or catalogs, extract high-frequency keywords (such as “artificial intelligence” and “psychology”), and infer subdivided interests. -**Tools**: Python's 'jieba` (Chinese word segmentation) or `NLTK` (English analysis) can be used for text analysis. 3. **Borrowing behavior pattern** -**Frequency**: High-frequency borrowing of certain types of books may reflect strong interest. -**Duration**: Long-term repeated borrowing of books in the same field may indicate a need for in-depth research. -**Timeline changes**: Interests may evolve over time, such as from “getting started with programming” to “artificial intelligence algorithms”. 4. **Association recommendation logic** -Similar to the e-commerce recommendation algorithm, it analyzes the co-occurrence of borrowing records (for example, people who borrow A book often borrow B book) and speculates on potential interest associations. --- ###**2. Limitations and challenges** 1. **Incomplete data** -Borrowing records only reflect “explicit behavior”, and e-books, offline purchases, or borrowing by others may be missed. -**Example**: Someone may borrow "Advanced Mathematics" only to cope with the exam, not for personal interest. 2. **Privacy and ethical risks** -Interest analysis without the user's consent may violate privacy policies (such as the "Personal Information Protection Law"). -Libraries usually need to keep borrowing records confidential, and only internal statistics (such as popular book rankings) rather than individual analysis. 3. **Possibility of misjudgment** -The borrowing motivation is complex (such as work requirements, random attempts), and it needs to be cross-verified in combination with other data (such as search records, questionnaire surveys). --- ###**3. Ethics and practical suggestions** 1. **Anonymous processing** -If it is used for academic research or service optimization, the data needs to be de-identified to avoid being associated with specific individuals. 2. **User authorization** -If the library provides personalized recommendation services, it should clearly inform the user and obtain authorization (such as “recommend related books based on your borrowing history”). 3. **Combine multidimensional data** -Integrate borrowing records, search keywords, activity participation (such as lecture topics), etc. to improve the accuracy of inference. --- ###**Conclusion** Technically, interest can be analyzed through borrowing history, but privacy issues need to be handled carefully, and the results are only probabilistic speculations. For libraries, it is more reasonable to provide personalized services (such as checking interest tags) that users choose independently, rather than extrapolating unilaterally.