In order to improve the outgoing quality of the telesignalization plug-in of the relay protection device and solve the problem of product inspection automation, this paper involves in a quality identification method of telesignalization plug-in based on PSO-MLP neural network. Firstly, the automation hardware test platform for telesignalization plug-in of relay protection device is established. Secondly, PSO algorithm is improved, and then the inertia weigh sliding characteristics is adjusted, which makes its real-time adjustment step by step according to particle spacing. Finally, the original start-up voltage data is examined by normality test using the k-s test in SPSS, then the frequency distribution of samples with normality and its fitting curve is obtained, to extract the characteristics of the training set and then to train and test the neural network. The experiment results show that the method can effectively and accurately identify the quality for telesignalization plug-in and realize the product inspection and quality identification automation and intelligent. The training time of PSO-MLP neural network is short, convergence rate is fast, and the identification accuracy is high, about 97%, and the generalization ability is strong. This work is supported by Tianjin Rail Transit Major Special Project (No. 18ZXGDGX00010) and Tianjin “Belt and Road” Initiative Science and Technology Innovation Cooperation Project (No. 18YDYGHZ00030).