The invention discloses a multi-spectral image classification method based on threshold self-adaptation and convolutional neural network, which inputs multi-spectral images of satellites to be classified in different time phases and different bands, and converts the marked parts of the same band images of all cities All pixels are normalized; the selected 9 bands are stacked into an image as a training data set; a classification model based on convolutional neural network is constructed, and the training data set is used to train the classification model to obtain a probability model based on OSM. The model and the confidence strategy adjust the softmax output results to obtain the final classification model, and finally upload the test results to the IEEE website to obtain the classification accuracy. The multi-spectral image classification method provided by the present invention makes full use of the characteristics of multi-spectral image with multiple bands, large data volume, and large information redundancy, and solves the problem that it is difficult to classify complex types of ground objects, and can not only improve the classification accuracy , Reduce the misclassification rate, and can also improve the classification speed.