Photosynthesis is the basis of plant growth and photosynthetic rate directly affecting the quality of fruit. The quantity and quality of tomato can be improved with the application of the appropriate amount of CO2, which is one of the principal raw material of photosynthesis. In this paper, photosynthetic rate prediction models under greenhouse condition in spring and autumn growth period were established respectively. The experimental data were collected during autumn of 2014 and spring of 2015. WSN was used to monitor greenhouse environmental parameters in real time, including air temperature, air humidity, CO2 concentration, soil temperature, soil moisture, and light intensity. An LI-6400XT portable photosynthesis analyzer was used to measure the photosynthetic rate of tomato plants, and the environmental information of leaves was controlled by small chamber environment. In order to verify the universality of the established model, three models using the data from both spring and autumn growth period, data only from spring growth period, and the data only from autumn growth period were established. The photosynthetic rate prediction models of single leaf were established based on the back propagation (BP) neural network. The environmental parameters were used as input neurons and the photosynthetic rate was taken as the output neuron. In order to improve the prediction accuracy of the model, the input neurons were standardized using Z score method and then processed by principal component analysis. Principal components were selected according to the principal components’ cumulative contribution rate. The photosynthetic rate prediction models of single leaf were established after principal components analysis and K-fold cross validation. The results indicated that the correlation coefficient of photosynthesis prediction model based on the data of spring 2015, autumn 2014 and the two seasons were 0.99, 0.95 and 0.85 respectively. The results of the models indicated that the universality of the model built using data from both seasons, and it has great potential for CO2 fertilizer control.