The effective extraction of features for hyperspectral image classification is a challenging research topic in remote sensing. To solve this problem, a spatial-spectral feature framework with an automatic threshold attribute attribute profile is proposed for hyperspectral image classification. The framework includes two stages. The first stage involves conversion of the grayscale value of the hyperspectral image into an attribute morphological profile of the tree structure, filtering the tree using the proposed automatic threshold method to create the final extended multivariate attribute morphological profile, and using the profile to obtain the spatial-spectral feature data. The proposed method does not require the customization of any thresholds and only requires a few filtering operations to obtain the maximum spatial information. Then, in the second stage, the derived spatial-spectral feature data are used to create an effective classifier using a trained spectral angle mapping stackable automatic encoder network to obtain the final classification result. Finally, the effectiveness of the method is verified by applying it to two real hyperspectral image datasets and comparing the results with those of existing methods.