The accurate prediction of greenhouse humidity is of great significance to the formulation of disease control strategies and automatic irrigation of water and fertilizer. In this paper, a prediction method based on multimodal data driven for full or Chinese names is studied. In order to decouple the complex relationship of environmental variables in greenhouse environmental control and improve the prediction efficiency of the model, this paper uses LASSO regression to screen the strongly related environmental factors of greenhouse air humidity changes from multiple greenhouse environmental parameters. Combining the advantages of CNN in extracting image spatial characteristics, based on GAF theory, the greenhouse time series are converted into two dimensional images of Gram angle summation field and Gram angle difference field, further enhancing effective information and suppressing environmental noise, The low complexity double convolution layer is introduced to fully extract the potential features of the image, identify the humidity change trend, and construct for the time series of different humidity change trends one by one Bayesian_ LSTM prediction model, increase smooth input to improve prediction accuracy. In this paper, the historical time series of indoor temperature, humidity and light intensity are converted into twodimensional images as input for cucumber greenhouse, and the prediction performance of the model is analyzed and verified. The experimental data shows that when the time sliding window size is 15, Gram angular difference field, Bayesian_ When the number of LSTM hidden nodes is 100, the average absolute error, average absolute percentage error, and root mean square error reach 258%, 456%, and 480% respectively, which is the best performance of the model. Compared with four mainstream prediction models, RNN, GRU, BiGRU and 1DCNN, the test results show good prediction performance.