点对点（peer-to-peer，P2P）交易为促进产消者可再生能源消纳与电力市场改革提供了新路径。然而，其依赖信息物理系统（cyber-physical system，CPS）传输信息，存在数据偏差、传输时滞等问题，加之产消者的信息保护需求，导致交易主体难以准确获知对方状态，形成信息不完全环境。同时，产消者在有限理性下的决策行为也加剧了交易不确定性。为此，提出一种不完全信息下基于随机博弈的P2P交易策略优化方法。首先，采用Harsanyi转换将不完全信息问题转化为完全但不完美信息，并结合前景理论修正转换结果以更贴合产消者实际心理偏好。其次，构建了产消者P2P交易随机博弈决策模型，并利用随机博弈中的Markov决策过程减少交易行为中的不确定性，从而提升交易策略的稳定性与有效性。之后，针对随机博弈中状态数量指数增长导致的“维数爆炸”问题，提出了一种状态树自适应削减技术，显著降低了计算复杂度。仿真结果表明，该方法能有效缓解不完全信息对交易的影响，降低行为不确定性，优化交易策略并提高经济效益。

Peer-to-peer （P2P） trading provides an important way for promoting the consumption of renewable energy by prosumers and the reform of power markets. The information transmission in P2P transactions relies on the cyber-physical system （CPS）, however, issues such as data bias, transmission delays, and prosumers' own information protection make it difficult for trading participants to accurately obtain the state information of other prosumers, thereby creating an incomplete information environment. In addition, the bounded rationality of prosumers intensifies the uncertainty of trading behavior. To address these issues, a stochastic game-based optimization method for prosumer P2P trading strategy considering incomplete information is proposed. Firstly, Harsanyi transformation is used to convert incomplete information into complete but imperfect information. The resulting transformation is then refined using prospect theory to better reflect prosumers' actual psychological preferences. Secondly, a stochastic game decision-making model for prosumer P2P trading is constructed. By incorporating the Markov decision process （MDP） framework into the stochastic game, behavioral uncertainty in trading is effectively reduced, enhancing the stability and effectiveness of trading strategies. Then，to mitigate the “curse of dimensionality” caused by the exponential growth of state spaces in the stochastic game, an adaptive state-tree pruning technique is proposed to significantly reduce computational complexity. Simulation results show that the proposed method effectively alleviates the impacts of incomplete information, reduces behavioral uncertainty, optimizes trading strategies, and improves overall economic performance.