In order to reduce the time consumption in solving the problem of large-scale nonlinear 0-1 programming, an intelligent algorithm acceleration strategy based on the improved Markov neighborhood was presented by analyzing the characteristics of nonlinear 0-1 programming and the Markov process of intelligent algorithm. First, a rewritten model of nonlinear 0-1 programming problem was given. Next, an improved Markov neighborhood was constructed based on the rewritten model, and the reachable probability between two random statuses with its conditions under the improved Markov neighborhood was derived and proven. With a further analysis of the structure of nonlinear 0-1 programming together with the improved Markov neighborhood, a recursive updating strategy of the constraint and objective function was designed to accelerate the intelligent algorithms. The experimental results illustrate that the proposed strategy improves the operating efficiency of intelligent algorithms while keeping a correspondence with the original algorithms in search results.