Over the past 30 years, climate change has caused obvious impacts on the catastrophic immigration of the brown plant hopper (BPH), Nilaparvata lugens (Stål). To further understand the impact of abnormal climate change on BPH immigration, this study collected data on the BPH using light traps at 35 plant protection stations in China from 1980 to 2016, and collected reanalyzed meteorological data from the National Center of Environmental Prediction (NCEP) from 1979 to 2016 to identify correlations between the occurrence grades of BPH and the meteorological factors affecting them; the key predicting factors were screened. The support vector machine (SVM) model, back propagation (BP) neural network, and regression analysis were used to establish medium long-term prediction models of the annual occurrence grades of BPH at the representative stations in south China, and their advantages and disadvantages were compared. The results were as follows:(1) Most of the abnormal climate occurrence areas in the Indochina Peninsula were distributed in the north. The occurrence frequency of abnormal climate in the north was higher than the frequency of the south, with the characteristics of the frequencies progressively descending from north to south in an annular pattern. (2) If the ground temperature of the Indochina Peninsula was higher than the average temperature and the relative humidity was greater than the average relative humidity during the 37 years, it brought about partially heavy or heavy occurrences of BPH immigration in south China. However, if the ground temperature of the Indochina Peninsula was lower than the average temperature and the relative humidity was less than the average relative humidity during the 37 years, it brought about partially light or light occurrences of BPH immigration in south China. (3) By comparing the correct rates of back substitution and prediction accuracy of all three models, we found that all three models had certain capabilities of predicting the occurrence grades of BPH in south China. The predicting capability of the SVM model was the best, the BP neural network was the second best, and the multiple linear regression model was the worst, indicating that the SVM model was more suitable for predicting the occurrence of BPH in rice production.