Given that the estimation of the initial priori hypothesis of the hidden Markov model (HMM) in the cross-station script detection is inaccurate, and the ability of the HMM parameter classification with the maximal likelihood criterion is poor, we propose a cross station script detection model based on MLP-HMM. Firstly, we use the natural language processing (NLP) approach to solve the high-dimensional complexity problem of data. Then, the weights of the whole model are fine-tuned to get the initial observation matrix through the multi-layer perceptron (MLP) neural network learning. Finally, the observation matrix is put into the HMM model to enhance the model's capacity of parameter construction and classification. Experimental results show that the HMM model combined with MLP can significantly improve the detection rate and reduce the detection time in comparison with the original HMM and the traditional algorithm in cross-site script detection.