Social intelligence manifests the capability, often referred to as the Theory of Mind (ToM), to discern others’ behavioral intentions, beliefs, and other mental states. ToM is crucial in multi-agent and human-machine interaction contexts, where each participant need grasp the mental states of others to respond, interact, and collaborate more effectively. Recent studies show that while the ToM model can infer beliefs, intentions, and predict future observations and actions, its application in complex tasks is significantly constrained. The challenges arise when the number of agents increases, the environment becomes more complex, and interacting with the environment and predicting the mental state of each other becomes difficult and time consuming. To overcome such limits, we take inspiration from the Theory of Collective Mind (ToCM) mechanism, predicting observations of all other agents into a unified but plural representation and discerning how our own actions affect this mental state representation. Based on this foundation, we construct an imaginative space to simulate the multi-agent interaction process, thus improving the efficiency of cooperation among multiple agents in complex decision-making environments. In various cooperative tasks with different numbers of agents, the experimental results highlight the superior cooperative efficiency and performance of our approach compared to the Multi-Agent Reinforcement Learning (MARL) baselines. We achieve consistent boost on SNN- and ANN-based decision networks, and demonstrate that ToCM’s inferences about others’ mental states can be transferred to new tasks for quickly and flexible adaptation.