How to use seismic data to effectively predict the distribution of seismic oil and gas reservoirs has always been the goal of the industry.. The P-wave and S-wave attributes in multicomponent seismic data not only contain a lot of information sensitive to oil and gas, but also have different responses to oil and gas. The problem of multiple solutions in seismic inversion can be effectively reduced using of these differences. Therefore, a multicomponent seismic reservoir prediction scheme based on self-organizing neural network was designed. Firstly, the seismic attributes sensitive to oil and gas are optimizated by cluster analysis. Then, the oil and gas characteristic information is extracted by composite operation. Finally, according to the input sample attribute data set, the network structure of self-organizing neural network is designed. The distance between neuron and sample is calculated to determine the best matching unit. The network weights are updated and adjusted to complete the network training. We assess the proposed method on real seismic data. The results show that the predicted results are basically consistent with the actual situation, which reduces differences and decrease in-terpretation ambiguity.