Pre-stack AVO inversion is one of the key methods for reservoir characterization, from which abundant elastic parameters in underground media can be obtained, which is conducive to the identification of oil and gas reservoirs. The inverse problem of pre-stack angular track set recording to elastic parameters is challenging in terms of adaptability and resolution. To solve these problems, a pre-stack AVO inversion network based on Transformer framework is proposed in this paper to solve the velocity and density of P-S wave. Inversion results are unstable and transverse continuity is poor in the network that uses pre-stack seismic data as one-way input. Therefore, prior knowledge constraints are introduced in training to improve the stability and accuracy of inversion results. In order to reduce the dependence on well data inversion, this paper uses transfer learning strategy to transfer the trained model to the real data inversion. In the data preprocessing stage, the data augmentation method is used to expand the training samples, so that the proposed network can fully extract the pre-stack trace set information, and establish the complex nonlinear mapping relationship between the pre-stack trace set and the elastic parameters. In this paper, the method of multi-task learning is used to realize simultaneous inversion of P-wave velocity, S-wave velocity and density, so as to improve the inversion accuracy and calculation efficiency. Through inversion testing of Marmousi2 synthetic data and actual data, and comparing with classical deep learning frameworks, the multi-task Transformer framework proposed in this paper has higher accuracy and high-resolution inversion results.