At present, algorithms for monitoring the driver’s line of sight area usually use deep learning models to directly classify image features. This method relies on the driver’ s line of sight area data collected from a fixed cockpit perspective. However, due to differences in driver appearance, sitting habits, and camera installation positions, it is difficult to obtain a large amount of comprehensive data, resulting in a decrease in classification accuracy. How to improve the accuracy of line of sight recognition using only small sample datasets has become a challenge. This article will design an adaptive line of sight region calibration method based on semi supervised learning theory. Firstly, the L2CS model is used to regress the two-dimensional vector of driver’ s line of sight angle in small sample data. Then, statistical analysis is used to mine the generalization prior knowledge of driver’ s line of sight angle and line of sight area mapping. This knowledge is used for line of sight area calibration, removing invalid line of sight landing points in non-inspection areas, and completing fine classification of driver’s personal line of sight area in a sliding window manner. Through experiments, it has been proven that this method solves the problem of low cross domain capability of end model data, improving accuracy and recall by 22. 4% and 10. 3% respectively, and the calibration results have adaptive adjustment ability.