Based on the immune response principle and operation mechanism, an immune evolutionary algorithm is discussed. According to the state transfer process of immune antibodies and Markov stochastic process, the immune evolutional algorithm convergence is analyzed by using the stochastic functional theory, which breaks the flaw of the traditional Markov chain method. The key factors that influencing the convergence rate and parameter selection principle are introduced by using functional theory and immune population state transformation process. Then the operation time complexity and the parameters are discussed through experiment comparison. The conclusion and theory developed in this paper shew new light on various practices and ideas employed by researches for larger and higher-dimensional optimization problem.