It is of great significance to recognize hand posture since people with hearing and speech disabilities use sign language as the main medium of communication. To eliminate the shortcoming of low recognition rate caused by the redundant features in the traditional 3D hand posture recognition methods, an algorithm of 3D hand posture recognition with space coordinates based on optimal feature selection is proposed in this paper, which innovatively combines with XGBoost method. And three main steps involved are feature extraction, optimal feature selection and posture recognition respectively. Firstly, self-defined attributes and features are extracted from 3D coordinate data collected by Leap Motion Controller. Then, the XGBoost model combined with cross validation is employed to select optimal features from different attributes. Finally, the selected features instead of all extracted features are then fed into Gaussian Naive Bayes classifier to recognize the target posture. The proposed method is experimented on different data sequences containing ten heavily-used postures of Chinese Sign Language. The experimental results show that after processed by optimal feature selection, the proposed method can achieve higher recognition rate than the traditional methods, and reduce the number of training samples by half at the peak recognition rate.