蒸发量是水文特征里的一个重要指标，为科学准确地分析及预测泰安市蒸发量的特点和走势，利用泰安市黄前水库、东周水库、大汶口和戴村坝4个代表性水文观测站1985—2021年的调查数据，通过Mann-Kendall检验法、滑动t检验法检测其突变特征后，使用R/S分析法预测未来蒸发量变化趋势。使用泰安站2005—2022年蒸发量日值观测数据，通过NeuralProphet算法耦合Optuna算法建模进行蒸发量的预测，并与其他预测模型的评价指标做出比较。结果表明：泰安市年及各季的蒸发量都呈现出明显的减少趋势，且在今后的一段时期内，大部分区域都将延续这样的发展态势。模型给出的预测数据准确率很高，符合要求，可以利用到日常生产及科研指导中，为蒸发量的预测提供了一种新途径。

Evaporation is considered an essential indicator within hydrological characteristics. To scientifically and accurately analyze and predict the characteristics and trends of evaporation in Tai'an City, data from four representative hydrological observation stations in Tai'an City—Huangqian Reservoir, Dongzhou Reservoir, Dawenkou, and Daicun Dam—from 1985 to 2021 were utilized. The abrupt change characteristics were analyzed through the Mann-Kendall test and sliding t-test, while the future evaporation trend was forecasted using the R/S analysis method. Daily evaporation observation data from the "Tai'an" station from 2005 to 2022 were employed, and a model coupling the NeuralProphet algorithm with the Optuna algorithm was developed for evaporation prediction. This model's predictive performance has been compared against other forecasting models' evaluation metrics. The findings indicate that the annual and seasonal evaporation rates in Tai'an City demonstrate a clear decreasing trend. For the foreseeable future, this trend is expected to continue in most areas. The forecasted data provided by the model exhibit high accuracy and meet the set standards, proving valuable for daily operations and scientific research guidance. This study offers a novel approach to predicting evaporation.