In order to quickly analyze varieties of tomato via space mutation breeding with near infrared spectra, firstly, principal component analysis was used to analyze the clustering of tomato leaf samples, and then abundant spectral data were compressed by wavelet transform and the model was built with radial basis function neural network, which offered a quantitative analysis of tomato varieties discrimination. The model regarded the compressed data as the input of neural network input vectors and the training process speeded up. One hundred and five leaf samples of CK, M1 and M2 were selected randomly to build the training model, and forty five samples formed the prediction set. The discrimination rate of 97.8% was achieved by this method. It offered a new approach to the fast discrimination of varieties of tomato via space mutation breeding.