Because of its strong expressive power and outstanding performance of classification, deep neural network (DNN), such as like convolution neural network (CNN), is widely used in various fields. When faced with high-dimensional features, DNNs are usually considered to have good robustness, for it can implicitly select relevant features. However, due to the huge number of parameters, if the data is not enough, the learning of neural network will be inadequate and the feature selection will not be desirable. DNN is a black box, which makes it difficult to observe what features are chosen and to evaluate its ability of feature selection. This paper proposes a feature contribution analysis method based on neuron receptive field. Using this method, the feature importance of a neural network, for example CNN, can be explicitly obtained. Further, the study finds that the neural network's ability in recognizing relevant and noise features is weaker than the tratitional evaluation methods. To enhance its feature selection ability, a feature selection enhanced CNN model is proposed to improve classification accuracy by applying traditional feature evaluation method to the learning process of neural network. In the task of the text-based user attribute modeling in social media, experimental results demonstrate the validity of the preoposed model.