Our study uses artificial intelligence and machine learning methods for dimensionality reduction, feature extraction, and classification of hyperspectral remote sensing images to detect subtle differences between different spectra. Our study also used hyperspectral remote sensing image data gathered by airborne visible infrared imaging spectrometer over the Indian Pines and Salinas test sites. Based on experimental analysis and comparison of mainstream classifiers and advanced algorithms, we propose a new classification mode for the hyperspectral image based on a two-channel fusion of convolutional neural network-support vector machine (CNN-SVM). This model gives full play to the advantages of CNN in feature extraction and the advantages of SVM in the classification. It also maximizes the generalization and accuracy of classification. The CNN is based on two-channel feature extraction, including spatial features and spectral features, which utilizes the characteristics of hyperspectral remote sensing images for classification. Experimental results show that the research is both theoretically and practically significant for improving the quality and accuracy of identification and classification.