储罐运营过程中不可避免将面临地震影响，造成罐内液体化学品的液位剧烈变化，为能提前防范因液位变化造成的运营事故，保障石化罐区长效稳定运行，提出一种液体动态特征驱动的外载荷作用下不同填充液位的罐壁液体爬升预测方法。设计二维缩尺试验模型以搭建储罐液动力模拟分析系统，在此基础上分析简谐激励下液舱内液动力响应规律，以不同条件下的液舱晃荡响应数据构建样本数据集，寻找共振状态下的最大液面上升值与稳态液位上升值。最后建立以液位、外激励为输入条件的多层感知机，选取40%~50%的样本作为训练集，剩余数据作为验证集进行储罐最大液位预测，实现不同储存液位下遭遇不同外界环境变化的储罐动力响应预警。通过分别对比两组的预测值与实验值，结果显示，不同条件下模型的预测误差小于4%，可实现基于给定外激励和液位情况下的罐体动力响应实时预测，模型相关系数值均接近于1，均方根误差值均小于2，相对偏差均在5%以内，具有可靠的准确性和泛化能力，可为后续工程应用提供依据。通过将传统储罐监测数据与深度学习技术相结合，实现了适用于不同液位和外载荷的储罐风险提前预测，为储罐内壁面环向压力分析和罐内液体自由面动态变化评价提供技术支撑，实现储罐风险早预知早防范。

During the operation of storage tanks, the impact of earthquakes is inevitable, leading to significant fluctuations in the liquid levels within the tanks. To proactively prevent operational accidents caused by such level changes and ensure the long-term stable operation of petrochemical tank farms, a dynamic characteristic-driven predictive strategy was developed to evaluate liquid climbing behavior on storage tank walls under external loads with varying fill levels. A scaled 2D experimental model was used to establish a hydrodynamic analysis system to study sloshing responses under harmonic excitation. A dataset was constructed, incorporating both resonance-induced maximum and steady-state liquid elevations. A multilayer perceptron (MLP) model was trained using 40% ~ 50% of the samples as the training set, with the remaining data used for validation. The model achieved prediction errors of less than 4%, an correlation coefficient value close to 1, root mean square error values below 2, and deviations under 5%. This deep learning-based framework enables real-time risk warnings for circumferential pressure calculations and free-surface dynamics assessments, thereby supporting proactive safety management of tanks under varying operating conditions.