为降低航空企业库存成本，考虑维修备件历史需求数据和特征数据，建立一个基于机器学习的端到端多周期库存优化模型。以库存总成本最小为目标，采用动态规划法确定历史最优补货决策；在此基础上构建端到端库存优化模型，其中以TimeXer作为需求预测模块，XGboost为库存决策模块，两者相互结合提高补货决策科学性。结合大型航空维修企业实例表明，该端到端模型可以有效降低库存成本，针对各类型消耗类航空备件均有较高的实际应用价值和可行性。

To reduce inventory costs for aviation enterprises, an end-to-end multi-period inventory optimization model based on machine learning was established, taking into account historical demand data and feature data for maintenance spare parts. The objective was to minimize total inventory costs, and dynamic programming is employed to determine the historical optimal replenishment decisions. Building on this foundation, an end-to-end inventory optimization model was constructed, where TimeXer served as the demand forecasting module and XGBoost functions as the inventory decision module. The integration of these two components enhanced the scientific basis of replenishment decisions. An example from a large aviation maintenance enterprise demonstrates that this end-to-end model effectively reduces inventory costs and exhibits high practical application value and feasibility for various types of consumable aviation spare parts.