This research carries out investigation on optimization of collaborative efficiency inside tourism supply chains, in which scenic spots, hotels, transport suppliers, online travel agents, catering and retail services, and tourist demand nodes interact with each other under fluctuant demand and time-sensitive capacity restriction conditions. One heterogeneous tourism supply-chain graph has been constructed, it is used for representing transaction relations, spatial closeness, capacity mutual complementarity, and disturbance spread. According to this graph, one intelligent decision-making model is established through integrating relation-conscious time requirement prediction, cooperative capability distribution, multi-agent movement selection, and constraint-conscious feasibility revision. We use public tourism statistics to carry out calibration work on the experimental dataset, and it is generated as daily operation samples that cover 12 destination cities in the period from 2019 to 2025. We have carried out comparison experiments with the Static CPFR, ARIMA-LP, LSTM-Heuristic, GNN-Optimizer, and MARL-CPFR baseline methods. The experiment results indicate that the method which we put forward obtains a MAPE of 6.52%, a service fulfillment rate of 95.1%, a capacity utilization rate of 82.4%, and a collaborative operation efficiency index of 89.6. When put in comparison with the best-performing baseline method, the method that we put forward makes forecasting error decrease by 12.0%, makes COEI rise by 4.9 points, and makes average response delay become shorter by 28.2%. Scene experiments further prove that our method still keeps stability under weekend peak flows, weather interferences, festival peak crowds, transportation limits, and combined interferences. The research results indicate that graph-based intelligent decision-making method can promote the conversion of demand prediction accuracy into implementable cooperative actions, hence providing operation support for destination management platforms, OTA resource distribution, and tourism emergency coordination work.
