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Wheat straw/polypropylene composites are green recycled and biomass-based material. After accelerated aging test of the composite was done, practical and effective methods for characterization and extraction of texture feature of microscopic Scanning Electron Microscopy (SEM) images of composites were investigated in this paper, and involved data compression and classification recognition were studied as well. Through Angle Measure Technique (AMT) method, the complexity spectra, MA spectra, of the preprocessed SEM images of the composites were derived and then the first four principal components of MA spectra using Principal Component Analysis (PCA) were extracted accordingly. Two kinds of classifiers based on Extreme Learning Machine (ELM) and Support Vector Machine (SVM) were introduced to classify the SEM images into five different aging periods in this paper. The research results indicate that AMT method is a very novel and effective approach in texture feature characterization and analysis of SEM images of composites and high classification accuracy of SEM images in different aging periods by using intelligent recognition can be reached.