ANALYSIS OF THE FLOWER APPRECIATION ROUTE AND ECONOMIC PROMOTION STRATEGY FOR THE "RAIN SCENE" DURING THE QINGMING FESTIVAL

Qingming Festival is an important traditional festival in China that combines cultural and economic value. The unpredictable weather in spring, especially the heavy rain, affects travel and flower viewing experiences. This article explores how to optimize Qingming flower viewing tourism under such weather conditions to improve economic benefits. This article proposes a quantitative analysis method based on the "rain shower" weather, which combines precipitation probability and precipitation amount to construct "rain shower" label data, and uses GRU recurrent neural network for weather classification prediction. In order to improve the accuracy of predictions, the SMOTE oversampling method was introduced, and the final model achieved an accuracy of 87.45% on the test set, providing a scientific basis for predicting the probability of rainfall during the Qingming Festival in 2026. Then, this article used a combination of deep modeling (GDD) and random forest regression to establish a flowering prediction model. The model simulates the flowering period based on temperature changes and integrates monitoring data from various regions to accurately predict the flowering periods of rapeseed flowers, cherry blossoms, and peonies. In the Qingming Festival of 2026, rapeseed flowers are the best viewing period in Wuyuan and Wuhan, peonies are in Luoyang and Xi'an, and cherry blossoms are approaching the end period, providing a basis for tourism planning. Finally, this article establishes a multi-objective path planning model aimed at optimizing the flower viewing experience. The model considers flowering period, weather, scenic spot rating, and transportation to construct a rating function. Under the three-day travel restriction, the best route was selected as "Luoyang Xi'an Turpan", which is suitable for the flowering period, comfortable in weather, and diverse in tourism. Adjustments can be made according to the actual situation.