In response to the problems of small existing chaos system parameter range and poor diffusion effect of the encrypted algorithm， a new cascaded chaotic system and a filter diffusion model were designed， and a color image encryption algorithm with unlimited image size was proposed and implemented. Firstly， a chaotic system named 2D-SIHC （Two-Dimensional Sine-Iterative-Henon Chaotic system） of was proposed with Henon mapping， Sine mapping and Iterative mapping as seed mappings， and a linear function was incorporated to extend the parameter range. In the two-dimensional sequence generated by this system， one dimension was employed to disturb pixel positions， the other dimension was applied to update filter templates and perform diffusion operations. Secondly， in order to avoid key reuse that reduces the algorithm’s security， the SHA-512 algorithm was combined with plaintext images to generate the key through the calculation of flag bits and weights. Thirdly， in order to enhance the algorithm's diffusion effect， a two-dimensional filter diffusion model was designed. Different from the traditional filter diffusion， which altered image pixel values by fixed template traversing， image pixel values were modified by the new diffusion model dynamically in the way of introducing chaotic sequences to update filter template values continuously. Finally， encryption was realized. Experimental results show that，taking the Airplane image as an example， the proposed algorithm can achieve the Number Pixel Change Rate （NPCR） of 99.605 9% and the Unified Average Change Intensity （UACI） of 33.397 1%， respectively， which are very close to the ideal values. In addition， the algorithm can resist a noise interference with 0.2 intensity and a cropping attack with 50% missing，and it has high encryption efficiency.

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.