Aiming at the problem of semi-focus images caused by improper focusing of far and near visual fields during digital image shooting， a multi-focus image fusion Network with Cascade fusion and enhanced reconstruction （CasNet） was proposed. Firstly， a cascade sampling module was constructed to calculate and merge the residuals of feature maps sampled at different depths for efficient utilization of focused features at different scales. Secondly， a lightweight multi-head self-attention mechanism was improved to perform dimensional residual calculation on feature maps for feature enhancement of the image and make the feature maps present better distribution in different dimensions. Thirdly， convolution channel attention stacking was used to complete feature reconstruction. Finally， interval convolution was used for up- and down-sampling during the sampling process， so as to retain more original image features. Experimental results demonstrate that CasNet achieves better results in metrics such as Average Gradient （AG） and Gray-Level Difference （GLD） on multi-focus image benchmark test sets Lytro， MFFW， grayscale， and MFI-WHU compared to popular methods such as SESF-Fuse （Spatially Enhanced Spatial Frequency-based Fusion） and U2Fusion （Unified Unsupervised Fusion network）.

In view of the problem that the existing exam paper generation technologies pay too much attention to the difficulty of generated exam papers， while ignoring other related objectives， such as quality， score distribution， and skill coverage， a multi-objective exam paper generation method guided by reinforcement learning and matrix completion was proposed to optimize the specific objectives in the field of exam paper generation. Firstly， deep knowledge tracking method was used to model the interaction information among students and response logs in order to obtain the skill proficiency of the student group. Secondly， matrix factorization and matrix completion methods were used to predict the scores of students' undone exercises. Finally， based on the multi-objective exam paper generation strategy， in order to improve the Q network update efficiency， an Exam Q-Network function approximator was designed to select the appropriate question set automatically for update of the exam paper composition. Experimental results show that compared with the models such as DEGA （Diseased-Enhanced Genetic Algorithm） and SSA-GA （Sparrow Search Algorithm - Genetic Algorithm）， it is verified that the proposed model has significant effect in solving multiple dilemmas of exam paper generation scenarios in terms of three indicators — difficulty， rationality and accuracy. The effect of verifying the models mentioned in the solution of the test papers is significantly effective.

Network traffic anomaly detection is a network security defense method that involves analyzing and determining network traffic to identify potential attacks. A new approach was proposed to address the issue of low detection accuracy and high false positive rate caused by imbalanced high-dimensional network traffic data and different attack categories. One Dimensional Convolutional Neural Network（1D-CNN） and Bidirectional Gated Recurrent Unit （BiGRU） were combined to construct a model for traffic anomaly detection. For class-imbalanced data， balanced processing was performed by using an improved Synthetic Minority Oversampling TEchnique （SMOTE）， namely Borderline-SMOTE， and an undersampling clustering technique based on Gaussian Mixture Model （GMM）. Subsequently， a one-dimensional CNN was utilized to extract local features in the data， and BiGRU was used to better extract the time series features in the data. Finally， the proposed model was evaluated on the UNSW-NB15 dataset， achieving an accuracy of 98.12% and a false positive rate of 1.28%. The experimental results demonstrate that the proposed model outperforms other classic machine learning and deep learning models， it improves the recognition rate for minority attacks and achieves higher detection accuracy.