Recently, multi-agent reinforcement learning (MARL) has achieved amazing performance on complex tasks. However, it still suffers from challenges of sparse rewards and contradiction between consistent cognition and policy diversity. In this paper, we propose novel methods for transferring knowledge from situation evaluation task to MARL task. Specifically, we utilize offline data from a single-agent scenario to train two situation evaluation models for: (1) constructing guiding dense rewards (GDR) in multi-agent scenarios to help agents explore real sparse rewards faster and jump out of locally optimal policies without changing the global optimal policy; (2) transferring a situation comprehension network (SCN) to multi-agent scenarios that balances the contradiction between consistent cognition and policy diversity among agents. Our methods can be easily combined with existing MARL methods. Empirical results show that our methods achieve state-of-the-art performance on Google Research Football which brings together above challenges.