Affective computing can provide a better teaching effectiveness and learning experience for intelligent education. Current research on affective computing in classroom domain still suffers from limited adaptability and weak perception on complex scenarios. To address these challenges， a novel hybrid architecture was proposed， namely SC-ACNet， aiming at accurate affective computing for students in classroom. In the architecture， the followings were included： a multi-scale student face detection module capable of adapting to small targets， an affective computing module with an adaptive spatial structure that can adapt to different facial postures to recognize five emotions （calm， confused， jolly， sleepy， and surprised） of students in classroom， and a self-attention module that visualized the regions of the model contributing most to the results. In addition， a new student classroom dataset， SC-ACD， was constructed to alleviate the lack of face emotion image datasets in classroom. Experimental results on SC-ACD dataset show that SC-ACNet improves the mean Average Precision （mAP） by 4.2 percentage points and the accuracy of affective computing by 9.1 percentage points compared with the baseline method YOLOv7. Furthermore， SC-ACNet has the accuracies of 0.972 and 0.994 on common sentiment datasets， namely KDEF and RaFD， validating the viability of the proposed method as a promising solution to elevate the quality of teaching and learning in intelligent classroom.

Focusing on the low quality of expansion source of traditional Pseudo Relevance Feedback (PRF) algorithms, which lead to low retrieval performance, a retrieval result based sorting model, namely REM, was proposed. Firstly, the first-pass retrieval result was considered as a pseudo relevant set. Secondly, documents in the pseudo relevant set were re-ranked based on rules of maximizing the relevance between the user query intention and the documents of pseudo relevant set and minimizing the similarity between documents. Finally, the top ranked documents of the re-ranking were regarded as the expansion source to the second-retrieval. The experimental results show that, compared with two classical PRF methods, the proposed model can improve the performance of retrieval and obtain more relevant feedback document to the user query intention.