Aiming at the situation of asynchronous events, state jump and complex node linkage in cross-border e-commerce logistics chain, a logistics risk early warning and supply chain collaboration framework based on timing feature analysis was constructed. Based on 138420 order performance records of 16 international transportation routes, 22 attributes such as node stay time, customs clearance interval, transfer frequency, warehouse processing time, distribution offset, transportation capacity status, regional load and abnormal event markers are extracted. Timestamp alignment, piecewise interpolation and min-max normalization are combined to complete data processing. The model consists of three parts: time series feature representation, risk warning and risk linkage. The event window aggregation, time offset calculation, stage position coding, bidirectional time series recognition and collaborative action sequencing are completed in turn. The results show that the proposed method reduces the average response delay to 1.84 s, and the congestion relief rate, performance recovery rate and cross-node consensus reach 21.7%, 90.8% and 92.4% respectively, and maintains a relatively stable warning output under different paths and different risk levels. It provides computational support for risk identification and collaborative scheduling in cross-border e-commerce logistics network and supports subsequent collaborative scheduling.
