To provide accurate meteorological forecasting services to crisp pear farmers in Dangshan County, Anhui Province, this article uses daily minimum temperature data from 5 orchards, A, B, C, D, and E, in the concentrated pear planting area of Dangshan County from March to April of 2017 to 2023, as well as daily minimum temperature data from the Dangshan National Basic Meteorological Observation Station. A simple linear regression equation was used to establish a meteorological forecasting model for the minimum temperature of crisp pear orchards during the flowering period, and this model was used to test and forecast the minimum temperature of crisp pear orchards during the flowering period in March to April 2024. The results showed that the 5 minimum temperature prediction models for orchards A, B, C, D, and E were as follows: y _A=0.917 6x _A-1.164 9，y _B=0.908 6x _B-0.342 6，y _C=0.984 1x _C-1.792 1，y _D=0.974 7x _D-0.314 6，y _E=0.928 4x _E-0.547 1, where x was the daily minimum temperature forecast value for Dangshan, and y was the daily minimum temperature forecast value for the orchard. The R ² values of the model tests were all greater than 0.8, indicating that the model had a good forecasting effect; the average absolute errors of the 5 minimum temperature prediction models for orchards were 1.6, 1.2, 1.4, 0.6, and 1.2 ℃, respectively, all of which were less than 2 ℃, and the average absolute error was smaller and more accurate than using the minimum temperature of Dangshan National Basic Meteorological Observatory as the minimum temperature prediction for orchards. The average absolute errors between the forecast results and the actual situation were 1.4, 1.1, 1.2, 0.8, and 1.1 ℃, respectively, all of which were less than 2 ℃, and were smaller than the average absolute error of using the lowest temperature forecast value of Dangshan as the lowest temperature forecast value for orchards, making the prediction more accurate. This indicates forecasting model for the orchard is convenient to use, has good forecasting effects, and can be applied to practical meteorological forecasting operations. This article provides a reference for improving the accuracy of regional daily minimum temperature forecasting.