As an important data preprocessing method, feature selection can not only solve the dimensionality disaster problem, but also improve the generalization ability of algorithms. A variety of methods have been applied to solve feature selection problems, where evolutionary computation techniques have recently gained much attention and shown some success. Recent study has shown that feature selection using forest optimization algorithm has better classification performance and dimensional reduction ability. However, the randomness of initialization phase and the artificial parameter setting of global seeding phase affect the accuracy and the dimension reduction ability of the algorithm. At the same time, the algorithm itself has the essential defect of insufficient high-dimensional data processing capability. In this study, an initialization strategy is given from the perspective of information gain rate, parameter is automatically generated by using simulated annealing temperature control function during global seeding, a fitness function is given by combining dimension reduction rate, using greedy algorithm to select the best tree from the high-quality forest obtained, and a feature selection algorithm EFSFOA (enhanced feature selection using forest optimization algorithm) is proposed. In addition, in the face of high-dimensional data processing, ensemble feature selection scheme is used to form an ensemble feature selection framework suitable for EFSFOA, so that it can effectively deal with the problem of high-dimensional data feature selection. Through designing some contrast experiments, it is verified that EFSFOA has significantly improved classification accuracy and dimensionality reduction rate compared with FSFOA, and the high-dimensional data processing capability has been increased to 100 000 dimensions. Comparing EFSFOA with other efficient evolutionary computation for feature selection approaches which have been proposed in recent years, EFSFOA still has strong competitiveness.