## 3 Methodology
### System Development
#### 3.3.2 KB Construction  
The KB module adopts a layered processing design philosophy, establishing a systematic knowledge processing and organization architecture to provide foundational support for diverse question generation. This study innovatively designed a three-layer processing architecture encompassing knowledge formatting, knowledge vectorization, and retrieval segmentation, achieving systematic transformation from raw educational resources to structured knowledge. This layered architectural design not only enhances the efficiency and accuracy of knowledge retrieval but also provides a solid data foundation for generating differentiated questions. At the knowledge formatting level, the system employs Optical Character Recognition (OCR) technology to convert various educational materials into standardized digital text. The knowledge formatting process is formally defined as:  
\[Format(K)=OCR(K_{raw}), \tag{1}\]  
where \(K_{raw}\) represents raw educational materials, including textbooks, teaching syllabi, and the professional literature. This standardization process ensures content consistency and lays the foundation for subsequent vectorization processing. The system optimizes \begin{table}
\begin{tabular}{l l} \hline
**Attribute** & **Description** \\ \hline Knowledge type & Four types of knowledge based on [PERSON]’s taxonomy, including factual, conceptual, procedural, and metacognitive \\ Cognitive level & The cognitive levels supported by each knowledge point, including remembering, understanding, applying, analyzing, evaluating, and creating \\ Knowledge content & Specific content of the knowledge point, including basic principles, fundamental concepts, basic methods, and important facts \\ \hline \end{tabular}
\end{table} 
Table 1: Knowledge point attributes. recognition results through multiple preprocessing mechanisms, including text segmentation, key information extraction, and format standardization, to enhance knowledge representation quality. At the knowledge vectorization stage, a hybrid vectorization strategy is employed to transform standardized text into high-dimensional vector representations. This transformation process is formally defined as:  
\[V(k)=vec(k), \tag{2}\]  
where \(k\) is a snippet of formatted knowledge from \(Format(K)\), and \(vec(k)\) represents the vectorized form of \(k\), utilizing models such as TF-IDF or Word2 Vec, or advanced techniques like BERT embeddings. The system innovatively designs a dynamic weight adjustment mechanism, adaptively adjusting weights of various vectorization techniques based on different knowledge types and application scenarios, enhancing knowledge representation accuracy. This vectorization method ensures that semantically similar knowledge points maintain proximity in vector space, providing a reliable foundation for subsequent similarity calculations and association analyses. At the retrieval segmentation level, the system systematically organizes vectorized knowledge based on predefined domain tags. The formal expression for retrieval segmentation is:  
\[S(k)=\cup_{i=1}^{n}segment(V(k_{i}),d_{i}), \tag{3}\]  
where \(S(k)\) represents the segmented KB, \(k_{i}\) are individual pieces of vectorized knowledge, \(d_{i}\) denotes the domain or knowledge point tags associated with each \(k_{i}\), and \(segment\) is the function that assigns each vectorized knowledge piece to its corresponding segment in the KB. The system designs a multi-level index structure, supporting rapid location and extraction of knowledge content from different dimensions while enabling knowledge recombination mechanisms to support diverse question generation requirements. This layered organization structure not only enhances knowledge retrieval efficiency but also provides flexible knowledge support for generating questions at different cognitive levels. This study constructed a comprehensive knowledge base system around core IT curriculum knowledge.