To solve the problems of high noise interference in the hyperspectral image itself and the process of classification, insufficient extraction of spatial-spectral feature information, and poor classification performance under limited samples, a hyperspectral image classification model SSFE-MBACNN based on multi-branched spatial-spectral feature enhancement is proposed. First, shallow spatial-spectral feature information and deep spatial feature information are extracted separately using multi-branch feature extraction modules, and attention mechanism are introduced to suppress noise interference. Second, an improved fusion module for multi-scale spatial-spectral feature extraction and a spatial feature enhancement module combining dual pooling and dilated convolution are designed to achieve spatial-spectral feature enhancement, reduce the number of model parameters and improve classification performance. Finally, the global average pooling layer is used instead of the fully connected layer to further reduce the number of parameters and alleviate the model overfitting problem. The experimental results show that the overall classification accuracies of 0.990 7, 0.997 5 and 0.994 7 are achieved for the Indian Pines (10% training sample), Pavia University (5% training sample) and Salinas (1% training sample) datasets. SSFE-MBACNN makes full use of the spatial-spectral feature information and achieves excellent classification performance with limited samples, which is significantly higher than other comparative methods.