Abstract: An equilibrium analysis method is a powerful tool for the operation efficiency analysis as well as mechanical design and demonstration of electricity market. However, how to consider the influence of contracts for differences（CFDs） and the risk preference of market players, and how to solve the multi-period equilibrium model efficiently have become urgent problems to be solved in the practical application of the equilibrium analysis method for electricity market. In this paper, a multi-period equilibrium analysis method for electricity market considering the risk management is put forward from the aspects of the determination of CFDs, the equilibrium modeling of the electricity market considering risk preference, and the model solving based on the multiagent deep reinforcement learning. In terms of the model framework, the methods for determining the contract price and curve reasonably based on the market equilibrium results are proposed for market-based CFDs and government-authorized CFDs,respectively; by using conditional value-at-risk to measure market risk, the stochastic optimization models for the offer decisions of generators are established; and the spot market clearing model is established by combining forward-looking security-constrained unit commitment（SCUC） and security-constrained economic dispatch（SCED） models to ensure the rationality of the results. For the solving algorithm, with the improvement of the deep reinforcement learning method, a risk-managing multi-agent deep reinforcement learning algorithm is proposed to solve the model iteratively. Finally, the numerical examples verify the rationality and effectiveness of the equilibrium analysis method, and the impact of different ratios of market-based CFDs or governmentauthorized CFDs, and different risk preferences on market equilibrium are analyzed.