In order to effectively solve the problems of low efficiency and low accuracy of manual testing of railway computer interlocking systems, a deep learning model for text localization and recognition of the upper computer interface of railway interlocking is proposed. First, by comparing the strategy of combining the text localization model based on CTPN (Connectionist Text Proposal Network) neural network with the four convolutional neural networks of ResNet50, AlexNet, ZF and VGG16, the optimal feature extraction network VGG16 was selected, which enhanced Convolutional feature maps represent the detailed features of high-level semantic information to facilitate the location of small text areas. Secondly, comparing the performance of common target detection models on the text localization effect and adopting the dropout method, the generalization ability and robustness of the CTPN network of the text localization model are improved. Then, the use of a combination of horizontal projection and vertical projection can effectively avoid problems such as text sticking on the interface of the interlocking upper computer. Finally, the improved AlexNet network is used to output the text recognition results. By verifying the text positioning and recognition data set of the interlocking host computer interface in the TensorFlow environment, the results show that the accuracy of the text positioning of the CTPN network in the railway interlocking host computer interface has reached 87.98%, the recall rate is 73.33%, and the average index of harmony is 80.39%; The improved AlexNet network achieves 89% accuracy in text recognition. It shows that the method in this paper can accurately locate and recognize the interface text of the railway computer interlocking upper computer, and provide reliable data support for automatic routing and automatic analysis of test results during the automatic interlocking test process.