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Multi-classification Twin Support Vector Machine model (MTWSVM) is put forward in order to solve the issues of low detection rates and weaken practicability in current network intrusion detection models. In the first place, the Adaptive Synthetic sampling method (ADASYN) is used to balance the dataset. And in the second place, the numerical and normalized methods are used to process the dataset. Ultimately, the data is balanced and simple enough to improve the calculability and convergence speed of the model. For the multi-classification problem, the classification strategy is used to build multi-classification model based on parameter optimization because the parameters have a greater impact on the model. The Improved Particle Swarm Optimization algorithm (IPSO) is used to find the global optimal parameters iteratively in the paper. The KDD’99 dataset is used to perform the network intrusion detection experiments. The experimental results show that the Multi-classification Twin Support Vector Machine model based on parameter optimization can effectively improve the detection performance compared with other models. At the same time, the experimental results show the overall detection accuracy and the detection accuracy of various attacks have been significantly improved. Therefore, the intrusion detection model proposed in the paper has validity and practicability in the network intrusion detection. [ABSTRACT FROM AUTHOR]