Predicting gas-bearing reservoirs from seismic data is a major field of research. Recently, deep learning has been applied to achieve better results; however, challenges such as high sample demand remain. Drilling data are often insufficient, and gas-bearing sample data are difficult to acquire in large quantities, particularly for low-level (new) exploration areas, which limits gas-bearing reservoir prediction by supervised learning. Therefore, this study proposes a hybrid model using unsupervised learning to extract seismic response features and predict gas-bearing reservoir distribution. First, seismic attribute analysis was used to extract as many longitudinal and transverse waves seismic attributes as possible to provide richer information about gas-bearing reservoirs. However, abundant seismic attributes contain a large amount of redundant information, thereby increasing computation time. Thus, principal component analysis (PCA) was used to reduce the dimensions of the extracted multi-component seismic attributes, remove redundant information, and obtain the most important characteristics that are sensitive to gas-bearing reservoirs. The sensitive seismic attributes obtained after the PCA were fed into a self-organizing neural network (SOM) for unsupervised learning. Neurons in the SOM compete with each other to optimize their own features and extract reservoir-sensitive seismic response features for prediction. To evaluate the proposed method, it was applied to part of the Marmousi2 model and real seismic data for validation. The following results were obtained: (1)The results of the synthetic data test results not only proved the effectiveness of the proposed method, but also showed that the method has strong noise resistance capability and is suitable for predicting gas-bearing reservoirs with low signal-to-noise ratio seismic data; (2) Based on the drilling data and sedimentary facies characteristics in the test area, the actual 3D seismic data test results showed that there are four wells that match the predicted results, and the distribution boundaries of the gas-bearing reservoirs they depict are relatively clear; (3) In low exploration areas (areas with few or no wells), this method is important as a guide to hydrocarbon exploration.