Complex single-objective optimization problem is a hot topic in the field of evolutionary computation. Differential evolution and covariance evolution are considered to be two of the most effective algorithms for this problem, as the difference information similar to the gradient can effectively guide the algorithm towards the optimal solution direction, and the covariance is based on statistics to generate an offspring population. In this paper, the covariance information is introduced to improve the difference operator, then an evolutionary algorithm based on neighborhood difference and covariance information (DEA/NC) is proposed to deal with complex single-objective optimization problem. The two commonly used difference operators generated by random selection individuals and combined by the current optimal solution are analyzed. With the first approach, the difference information cannot be used as a local gradient information to guide the search of the algorithm when the Euclidean distance between randomly selected individuals is large. Meanwhile, the second approach will make the population search in the direction of the current optimal solution, which will lead the population to quickly fall into local optimum. In this paper, a neighborhood difference method is proposed to improve the effectiveness of the differential operator while avoiding the diversity of population loss. In addition, a covariance is introduced to measure the correlation between individual variables, and the correlation is used to optimize the difference operator. Finally, the algorithm tests the single-objective optimization problem in cec2014, and compares the results with the existing differential evolution algorithms. The experimental results show the effectiveness of the proposed algorithm.