Classification of stellar spectral data is one of the most basic tasks in automatic recognition of celestial spectra. The study of spectral classification can provide clues to the evolution of stars. With the development of science and technology, astronomical data are also moving towards the era of big data. The number of stars that need to be processed is increasing. How to classify them automatically and accurately has become one of the difficult problems that astronomers have to solve. At present, there are few methods to solve the problem of Star automatic classification. In this paper, a convolution neural network based method is used to classify star spectral MK system. The network is composed of data input layer, four convolution layers, four pooling layers, full connection layer and output layer. Compared with traditional network, it has the advantages of local perception and parameter sharing. In this paper, a simple and efficient convolution neural network with four convolution layers is constructed by Tensorflow in Python 3.5 environment. Dropout is applied to the full connection layer to prevent over fitting. Dropout’s basic idea: When the network model is trained, some neural network nodes are discarded in a certain proportion, so that they do not play a role temporarily. Dropout can be understood as a very efficient neural network model averaging method, because it does not depend on some local features, it can make the network model more robust. The one-dimensional star spectrogram used in the experiment was downloaded from the LAMOST DR3 database. First, the spectrum was intercepted by pretreatment. After uniform sampling, it was initialized by min-max standardization method. The experiment consists of two parts. The first part classifies the spectrum according to the star spectrum MK system. Each training sample contains 1 000 spectral data and 400 spectral data. First, the CNN network is trained by training samples, and then 3 000 iterations are carried out. Then, the test samples are divided into several parts by the trained network. The second part is the classification of adjacent two types of star spectra, in which the O-type star data set sample is 250 spectra, and the rest are 4 000 spectra. The data are divided into five parts, one of which is selected as test set each time, the rest as training set, using 5 fold crossover. The accuracy of the model was calculated by the verification method, and the BP neural network was used for comparative experiments. The indicators to evaluate the network model include accuracy rate P, recall rate R, F-score and accuracy rate A. The experimental results show that the classification accuracy of the six types of stars is more than 95%. When classifying the adjacent types of stars, the classification results are not ideal because of the small sample size of O type stars. The classification accuracy of the other types of stars is higher than 98%. All the above results prove that CNN algorithm can classify the stars. The classification of stellar spectra is well solved.