With the rapid development of distributed generation and microgrid technologies, energy trading strategies in microgrids have become crucial for enhancing clean energy utilization and promoting microgrid construction. Incomplete information in the energy trading process leads to decision-making uncertainty among participants, which affects the stability and reliability of energy supply. To address this issue, this paper investigates microgrid energy trading strategies under incomplete information based on networked evolutionary game theory. First, a networked evolutionary game model for microgrid energy trading under incomplete information is established based on the strategy updating rules of microgrids and the main grid operator. This model effectively captures the strategy evolution dynamics of trading participants under incomplete information. Second, using the semi-tensor product of matrices, the algebraic form of the energy trading model based on networked evolutionary game theory is derived. On this basis, an algorithm for computing the game equilibrium set and a criterion for determining the convergence of the energy trading model are proposed. Finally, a simulation example verifies the effectiveness of the proposed results. The proposed method effectively analyzes the impact of incomplete information on the evolution of microgrid energy trading strategies and the convergence of the energy trading model.