The photosynthetic product accumulation of cucumber, which is related to temperature, carbon dioxide concentration and light intensity, shows high correlation with the photosynthetic characteristics of different leaf positions. The dynamic acquisition of light parameters in different leaf positionsis a key problem to be solved in the optimization and regulation of stereo light environment of facility cucumber.A stereo light environment optimization control modelwas proposed based on multiintelligent algorithm for cucumber, a multifactor nesting experiment was designed to obtain multidimensional sample data, and a regression support vector machine photosynthetic rate prediction model, which coupled leaf position, temperature, light intensity and carbon dioxide concentration was constructed. Based on the particle swarm optimization algorithm, the light saturation point of different leaf positions was obtained under different environmental conditions. The regression support vector machine algorithm was used to construct the stereo light environment optimization control model for the target light intensity. The verification results showed that the proposed method can dynamically calculate the target light intensity with different environmental factors of different parts of the whole crop. The coefficient of determination was 0.9993, and the root mean square error was 2.349μmol/(m2·s). The relationship between leaf chlorophyll content and photosynthetic characteristics of different leaf position was further analyzed, and the strong correlation between the two was proved. The research result had important significance for improving the efficiency of stereo light environment regulation of facility vine crops.