To improve the accuracy of protein-ATP (Adenosine TriPhosphate) binding sites, an algorithm was proposed by using One Dimensional Convolutional Neural Network (1D-CNN). Firstly, based on the protein sequence information, position specific score matrix information, secondary structure information and water solubility information were combined and random under-sampling was used to eliminate the impact of data imbalance. Then, the missing features were completed by recoding. Finally, the training features were obtained. A 1D-CNN was trained to predict protein-ATP binding sites, the network structure was optimized, and experiments were carried out to compare the proposed method and other machine learning methods. Experimental results show that the proposed method is effective and can achieve better performance on AUC (Area Under Curve) compared to the traditional Support Vector Machine (SVM).