The present invention provides a hyper-parameter determination method for the critical convolutional layer of a remote-sensing classification convolution neural network. The method comprises the steps of constructing a convolutional neural network sample set; constructing a convolutional neural network structure; deterring the hyper-parameters of the critical layer of the convolutional neural network; selecting one convolutional layer as the critical layer, presetting the convolutional kernel size of the critical layer, and calculating the convolution scale; based on the convolutional kernel of the critical layer and the convolution scale, calculating the convolution step length according to a preset rule; presetting the convolutional kernels of other convolutional layers to be kernel size, and presetting the convolution step lengths of other convolutional layers to be 1; and conducting the mean-value down-sampling or the maximum-value down-sampling as the subsequent down-sampling. According to the technical scheme of the invention, based on the image input size and the convolution kernel size, the convolution scale concept is proposed and is adaptive to the remote-sensing spatial scale. On the above basis, an input size and convolution scale-based method for jointly determining the hyper-parameters of the critical layers is provided. In this way, the parameter adjustment time required for the algorithm is reduced, and the object-oriented remote-sensing classification precision is improved.