Abstract: To accurately predict the yield of tobacco in Shaoyang, soil samples from 70 tobacco growing areas were taken as the research objects. Firstly, the principal component analysis (PCA) was used to analyze the impact of 19 soil nutrient indexes on tobacco yield. The results showed that the yield of tobacco in Shaoyang was mainly affected by 9 soil nutrient indexes, including organic matter, available zinc, available boron, available manganese, available sulfur, exchangeable calcium, total potassium, available iron and available potassium. After that, support vector regression (SVR) was used to predict the tobacco yield of the 70 tobacco growing areas in Shaoyang. The results showed that the mean square error (MSE) of prediction results with 9 soil nutrient indexes was significantly less than the MSE of prediction results with 19 soil nutrient indexes. Finally, compared with that of random forest regression algorithm, the prediction accuracy of SVR was obviously better. The method based on principal component analysis and support vector regression is effective to predict tobacco yield in Shaoyang.