To address the problems of low multi-classification accuracy， poor generalization， and easy privacy invasion in traditional encrypted traffic identification methods， a multi-classification deep learning model that combines Attention mechanism （Attention） with one-Dimensional Convolutional Neural Network （1DCNN） was proposed， namely Attention-1DCNN-CE. This model consists of three core components： 1） in the dataset preprocessing stage， the spatial relationship among packets in the original data stream was retained， and a cost-sensitive matrix was constructed on the basis of the sample distribution； 2） based on the preliminary extraction of encrypted traffic features， the Attention and 1DCNN models were used to mine deeply and compress the global and local features of the traffic； 3） in response to the challenge of data imbalance， by combining the cost-sensitive matrix with the Cross Entropy （CE） loss function， the sample classification accuracy of minority class was improved significantly， thereby optimizing the overall performance of the model. Experimental results show that on BOT-IOT and TON-IOT datasets， the overall identification accuracy of this model is higher than 97%. Additionally， on public datasets ISCX-VPN and USTC-TFC， this model performs excellently， and achieves performance similar to that of ET-BERT （Encrypted Traffic BERT） without the need for pre-training. Compared to Payload Encoding Representation from Transformer （PERT） on ISCX-VPN dataset， this model improves the F1 score in application type detection by 29.9 percentage points. The above validates the effectiveness of this model， so that this model provides a solution for encrypted traffic identification and malicious traffic detection.

In text matching tasks， the complexity and diversity of textual data often lead to issues of lacking robustness during training. Traditional methods to address the lack of robustness， such as data augmentation and regularization， can be effective， but are often only applicable to specific types of noise or disturbances， and require a lot of computational resources. Therefore， a method based on Meta-Weight network （MW-net） — Meta-Weight network improved by the Covariance matrix （CovMW-net） was proposed. Firstly， the weight parameters and loss functions were adjusted by learning adaptively， thereby realizing rapid and reasonable weight distribution. Then， by controlling the weights of samples， the impacts of samples on training effects were magnified or diminished， and ultimately the training robustness was enhanced. The meta-learning framework of MW-net was inherited by CovMW-net， thereby saving computational resources. At the same time， by CovMW-net， through incorporating covariance matrices， deep feature extraction for samples in each category was conducted， and the covariance matrices of these features were calculated to measure minority class data， thereby mitigating the negative impacts of long-tail distributions caused by random sampling from meta-datasets in MW-net. Experimental results on the Clothing1M dataset show that CovMW-net outperforms the original method MW-net by 0.86 percentage points in accuracy and outperforms all comparative methods. In addition， on the Large-scale Chinese Question Matching Corpus （LCQMC） and Baidu Question-answer matching dataset （BQ）， CovMW-net has the accuracy improvements between 4 and 6 percentage points mostly compared to the baseline. It can be seen that CovMW-net is effective in dealing with biases in meta-datasets and is feasible for application in research on the robustness of text matching.

To avoid the limitations of the traditional fuzzy rule based on Genetic Algorithm (GA), a calculation method of fuzzy control rule which contains weight coefficient was presented. GA was used to find the best weight coefficient which calculate the fuzzy rules. In this method, different weight coefficients could be provided according to different input levels, the correlation and symmetry of the weight coefficients could be used to assess all the fuzzy rules and then reduce the influence of the invalid rules. The performance comparison experiments show that the system which consists of these fuzzy rules has small overshoot, short adjustment time, and practical applications in fuzzy control. The experiments of different stimulus signals show that the system which consists of these fuzzy rules doesnt rely on stimulus signal as well as having a good tracking effect and stronger robustness.