Tibetan Jiu Chess, the national intangible cultural heritage of China, involves complex gameplay with both layout and battle stages, presenting a vast action space. Deep learning effectiveness is often limited by computational and data constraints. To address this, we propose LSAFJiu, a low-resource algorithm based on long sequence action forecasting (LSAF). Its innovation combines an LSAF model, a policy network, and a value network to evaluate game tree leaf nodes. The LSAF model incorporates the ActionFormer for the layout stage and an action-state autoencoding planner for the battle stage, aimed at enhancing decision-making capabilities while reducing resource consumption. The policy and value networks are built on the ResNet50 architecture, utilizing a serial stacked activation function to minimize hardware requirements and improve prediction accuracy. To train the initial policy network, 20000 high-quality game instances were generated by three intelligent programs grounded in human expertise, effectively reducing self-play training time. Experiments conducted on two 2060 GPUs demonstrated that LSAFJiu increased the winning rate by 30.5% while requiring only 2.51% of the simulation time needed by the policy network alone. Additionally, the serial stacked activation function improved prediction accuracy by at least 2%. The overall winning rate of LSAFJiu reached 88% within a reasonable decision time. LSAFJiu is an efficient, low-resource algorithm that offers accurate decision-making for Tibetan Jiu Chess.