Feature selection is a hot issue in the field of machine learning. Meta-heuristic algorithm is one of the important methods of feature selection, and its performance will have a direct impact on problem solving. Crow search algorithm (CSA) is a kind of meta-heuristic algorithm inspired by the behavior of crow intelligent group. Because of its simple and efficient characteristics, it is used by many scholars to solve the feature selection problem. However, CSA is easy to fall into a local optimal solution and the convergence speed is slow, which severely limits the algorithm's solving ability. In response to this problem, this study uses three operators, namely, logistic chaotic mapping, opposition-based learning method, and differential evolution, combined with crow search algorithm, proposes a feature selection algorithm BICSA to select the optimal feature subset. In the experimental phase, the performance of BICSA was demonstrated by using 16 data sets in the UCI database. Experimental results show that compared with other feature selection algorithms, the feature subset obtained by BICSA has higher classification accuracy and higher dimensional compression capabilities, indicating that BICSA has the ability to deal with feature selection problems with strong competitiveness and sufficient superiority.