This study is to find the factors that affect the spatial change of forest land and purposefully predict the evolution trend of forest land space, so as to facilitate the rural planning work. The rural forest land situation in Zhangjiakou City of Hebei Province is analyzed, and the future evolution and development of forest space are predicted through analysis of correlation between the forest land influencing factors and the forest land productivity. Meanwhile, the multiple linear regression (MLR) prediction algorithm and support vector machine (SVM) are compared to obtain a more accurate prediction algorithm, which provides a strong basis for rural planning. The research results show that the annual rainfall and rainfall erosion have poor correlation with the spatial evolution of forest land relatively; while the average annual temperature is negatively correlated with annual rainfall and the rainfall erosivity. In addition, the soil erosion and terrain undulation of forest land have higher correlations with the rainfall erosivity due to abundant rainfall. The steeper the slope, the less human interference. What’s more, the prediction value of SVM is closer to the actual value with smaller absolute error, so it is more accurate than MLR. Therefore, research on the prediction algorithm provides new ideas for enriching the prediction algorithms of the spatial evolution trend, and is of great significance for improving the forest resource reserve capacity and meeting more forest resource demand in China. In addition, it can optimize the natural environmental quality, so it can be applied to rural planning and construction.

本研究旨在发现影响林地空间变化的因素，有目的地预测林地空间的演变趋势，以利于农村规划工作。通过分析林地影响因素与林地生产力之间的相关性，分析了河北省张家口市农村林地状况，并预测了林地未来的演变和发展。同时，比较了多元线性回归（MLR）预测算法和支持向量机（SVM）以获得更准确的预测算法，为农村规划提供了坚实的基础。研究结果表明，年降水量和降雨侵蚀量与林地空间演变的相关性相对较弱。年平均温度与年降水量和降雨侵蚀力呈负相关。此外，由于降雨充沛，林地的水土流失和地形起伏与降雨侵蚀力的相关性更高。坡度越陡，人为干扰就越少。此外，SVM的预测值更接近于实际值，且绝对误差较小，因此比MLR更准确。因此，对预测算法的研究为丰富空间演化趋势的预测算法提供了新思路，对提高我国森林资源储备能力和满足更多森林资源需求具有重要意义。此外，它可以优化自然环境质量，因此可以应用于农村规划和建设。