With the rapid development of Big data technology and artificial intelligence, in view of the problems such as low accuracy of the current jujube yield prediction model and long optimization time of the model, the jujube yield in Shanxi Province from 1993 to 2020 and the factors in 17 dimensions were taken as the basic data, a fruit fly algorithm optimized support vector regression (PCA-FOA-SVR) model was proposed for predicting jujube yield based on principal component analysis. Firstly, principal component analysis (PCA) was used to reduce the dimensionality of the data, with 5-dimensional indicators as input variables and production as output variables. Secondly, based on the support vector machine regression model (SVR), the fruit fly optimization algorithm (FOA) was used to optimize the SVR parameter penalty factor c and kernel function parameter g, and a PCA-FOA-SVR model was constructed. The test results were verified. It is found that the root-mean-square deviation (RMSE), mean absolute error (MAE), and coefficient of determination R² of PCA-FOA-SVR are 3.11, 3.01, 0.96, respectively, and the indicators of SVR are 5.33, 4.07, 0.9, increased by 41.7%, 26%, and 6.7%, respectively. Finally, GM (1,1) was used to predict the data of various dimensions, and the PCA-FOA-SVR model was used to predict the jujube production in Shanxi Province in the next 10 years. The results show that the jujube production will reach a peak in 2025, which provides a certain scientific basis for subsequent related research.