Aiming at the problem of stereoscopic image quality prediction bias, a lightweight stereoscopic image quality assessment method combining peripheral visual information is proposed based on the human eye vision model. First, a binocular perception model is constructed to acquire the central concave visual area and the peripheral visual area, and a symmetric stereoscopic information fusion module is used to enhance the parallax information. Then, the binocular quality perception features are obtained by the lightweight feature extraction module. Finally, the relationship between the subjective and objective stereoscopic image quality evaluation values maps in the fully connected layer. An adaptive multi-loss strategy is introduced to guide the model training, while the performance tests are conducted in LIVE 3D and the Waterloo IVC stereoscopic image databases. The results show that the proposed algorithm performs comprehensive and maintains a high level of consistency with human subjective quality perception.