Abstract: Accurate prediction of first flowering dates is an important basis for the flower management and blossom festival activities of Dangshansu pear. Used to linear trend analysis, the phenological and meteorological data of Dangshansu pear were used to analyze the annual fluctuation trend of blossom from 1983 to 2018. Correlation analysis was used to screen the key meteorological factors affecting the first flowering dates and the characteristic variable set was formed according to different forecast dates. The random forest algorithm (RF) was used to construct a daily rolling prediction model of the first flowering dates. Starting from March 11th to March 25 th, the random forest algorithm (RF) was used to train one forecast model every day to realize the daily rolling weather forecast of the blossom period. The results showed that: 1) The first flowering date from 1983 to 2018 showed an extremely significant advanced trend (P<0.001), about 2.750 days earlier in every 10a. 2) Among the 16 daily weather forecasting models, a total of 200 meteorological factors were closely related to the first flowering date. There were the average temperature from mid- February to late February, the average temperature from mid- February to early March, the average temperature from mid- February to mid-March, the average temperature from early- March to mid-March, the average minimum temperature of late February and mid-March, the average max temperature of middle and late February, the active accumulated temperature of the days before flowering in different periods ≥0℃ and ≥3℃, and the effective accumulated temperature of the days before flowering in different periods ≥3℃, ≥5℃ and ≥7.2℃. The correlation coefficient |r| was between 0.469-0.789. Among them, the closer the accumulated temperature of different boundary effect variables were to the early and late flowering period, the higher the correlation degree was. 3) From March 10th to March 25 th, a total of 16 beginning flowering day by day forecast model of training set and test set. Among them, 11 feature variables were included in 10 models from March 10 to 19, and 15 feature variables were included in six models from March 20th to 25th. The average accuracy (Nd) of the training set and the test set of the weather forecasting model were 92.9% and 75.5% respectively. Their mean Root Mean Square Error (RMSE) were 1.693-2.870 and 2.240-7.237. Their mean decision coefficients (R2) were 0.891 and 0.701 respectively. From March 10th to March 25th, the determination coefficients of each model test set and training set of meteorological forecast gradually increased as the forecast date approached the actual blossom period. 4) Among the experimental weather forecasts for 2019, the prediction accuracy of 16 weather forecasting models was relatively high, among which the prediction values of the nine forecast days starting from March 17 and following were completely consistent with the actual conditions. This study showed that the RF algorithm had a certain operational application potential in the daily weather forecast of pear blossom period. The forecast accuracy basically reached the demand of sufficient weather service.