The invention provides an automatic convolution kernel size determining convolutional neural network-based hyperspectral image classification method. The method comprises the following steps of: firstly clustering a plurality of image block samples with different sizes by using a K-means algorithm; designing a new evaluation index for a clustering result of the samples, the sizes of which are different and category quantities of which are uniformly distributed, and evaluating the clustering the result of the samples with different sizes by using the evaluation index, and determining a self-adaptive convolution kernel according to the evaluation result; and finally training a convolutional neural network of the self-adaptive convolution kernel, and classifying hyperspectral images by utilizing the trained network. Self-adaptive convolution kernels obtained by clustering and index evaluation are capable of representing data information more effectively, so that better hyperspectral imageclassification results can be obtained by utilizing the method.