A robust moving horizon estimation algorithm is proposed for the state estimation of multi-agent systems under deceptive attacks and uncertain parameter constraints. Firstly，the deceptive attacks are characterized as a stochastic sequence satisfying the Bernoulli distribution，and a predictive compensation strategy is adopted. Secondly，the time-varying weight matrix in the distributed moving horizon estimation algorithm is modified to counteract the influence of uncertain parameters. Based on this，a robust moving horizon estimator is obtained by solving an min-max optimization problem. Subsequently，conclusions regarding the convergence of estimation error are given. Finally，simulation results of a multi-agent system composed of vehicles verify the feasibility of the proposed algorithm.