In the complex environment of strong confrontation, the entity perception information is incomplete and the real-time response is required, which poses a challenge to the long-term and forward-looking dynamic force deployment decision. How to realize efficient exploration of strategies through explainable effective rewards and incentives is the key to drive strategic planning of dynamic force deployment by using learning methods. Aiming at the dynamic force deployment problem, this paper first proposes a multi-agent reinforcement learning strategy planning method based on SVD (Shapley value decomposition). The reward distribution among cooperative multi-agents is explained by SVD, and the reward distribution is analysed by SVD reinforcement learning method to solve Markov convex game strategy; Secondly, based on the scenario of naval and air cross-domain cooperative confrontation, this paper analyses the allocation of space domain combat resources in heterogeneous multi-entity cooperative confrontation, builds a dynamic force deployment strategy planning model, and designs the state space, action space and reward function of the problem. Finally, based on typical application scenarios, simulation experiments are organized to verify the dynamic force deployment problem with the military chess deduction system. The results show that compared with the multi-class baseline algorithm, the proposed method in this paper has excellent performance in strategic planning of dynamic force deployment, and it is theoretically interpretable. The proposed method learned the strategy of "layer upon layer interception, zone confrontation, cover core, and layered breaking". The method of project address: https://gitee.com/jrluo2049/shapleymarl.