Most hyperspectral unmixing methods based on autoencoders mainly focus either on the spatial information or on the spectral information, while ignoring the balance extraction of them. To address this issue, we propose a hyperspectral image unmixing method based on autoencoders and multi-scale spatial-spectral feature encoding. This method utilizes a CNN encoder for multi-scale unmixing feature extraction. The Transformer encoder receives the multi-scale unmixing features. It further utilizes sub-Transformer encoders and a global Transformer encoder to decouple the dependence between spatial and spectral information. Experimental analysis is conducted on two real datasets to validate the performance of the proposed method. The results demonstrate that the proposed unmixing algorithm can improve the accuracy of hyperspectral image unmixing.