In recent years, a number of intelligent optimization algorithms have been put forth; researchers have combined various intelligent optimization algorithms for image processing, data analyses, fault detection, feature selection, support vector machines, wireless sensors, neural networks, and other technological fields [10; 11; 12; 13]. Intelligent optimization algorithms have an excellent ability to solve path-planning problems and have been widely used in machine learning [14; 15; 16; 17]. Common intelligent optimization techniques include the Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization, Whale Shark Optimization, and so forth [18; 19; 20; 21; 22; 23]. These algorithms have become powerful tools to search the optimal parameters of different models, because they have different search strategies and the ability to adjust parameters. In previous research, efforts have been made to predict pork prices by integrating optimization algorithms with artificial intelligence techniques. Ref. [24] proposed a chaotic neural network model based on the Genetic Algorithm (GA)-CNN; it optimized the connection weights between the nodes in the input layer, hidden layer, and output layer. According to the experimental findings, by using the GA-CNN model to predict 1603 daily sample figures, the relative error between the predicted data and real data is less than 0.5%, proving that the GA-CNN has a very excellent performance [25; 26; 27]. Therefore, in order to further enhance the prediction accuracy of pork prices, our study adopts the CART decision tree technique and proposes a pig price prediction model based on parameter optimization using the Sparrow Search Algorithm (SSA). The proposed model addresses the impact of uncertainty in the parameter settings on the prediction accuracy and accelerates model convergence. By incorporating an optimized iterative combination and hybrid algorithm into the CART model, this experiment proves that the proposed method can obtain the optimal MinLeafSize without empirical guidance, which reduces the defects of human experience in selecting the MinLeafSize of the decision tree, and allows the optimized CART model to achieve good predictive performance. The main contributions of this paper are as follows:  
1. This paper first uses the latest optimization algorithm, SSA (Sparrow Search Algorithm), to optimize the MinLeafSize of the decision tree, reduce the overfitting risk of the model, overcome the limitations of single model prediction, and explore a new path to solve the problem of the easy overfitting of the decision tree. 2. In this paper, the correlation coefficients between various variables are measured using the Pearson correlation coefficient and visually expressed via thermal maps. 3. Different from the traditional method of optimizing hyperparameters on the training set, this study divided the dataset into three categories and updated the search strategy by calculating the MAE values of the validation set and the training set. 4. We compared the performance of multiple combination models by combining different optimization algorithms and decision trees, and found an optimal combination model for pork price prediction. 5. Compared with the traditional Classification and Regression Trees (CART) model, SA-CART model, and WSO-CART model, the proposed model has a higher prediction accuracy. Like the SSA-CART combination model, it is more suitable for the current pork price dataset. This algorithm not only improved the accuracy of the pork price forecasting model, but also provided a new method for pork price forecasting. In general, when compared with the existing algorithms, this model has significantly improved the search accuracy, convergence speed, stability, and avoidance of local optimal values of pork price prediction. The main process is depicted in Figure 1. The initial step of the data preprocessing includes the data preparation, data standardization, and data splitting.