Peptide drugs are generally compounds with less than 100 amino acids connected by peptide bonds and having drug effects. Because of their unique advantages such as significant activity, strong specificity, and low toxicity, they are widely applied in the treatment of various diseases. The design and development of new peptide drugs have broad prospects, and determining the molecular characteristics of disease-related peptide drugs is the key to drug design. This research takes anti-cancer peptides and anti-hypertensive peptides as the research objects, and we propose a novel method of describing peptide drugs, making use of the topological attribute values in an amino acid interaction network to represent the characteristics of peptides. In addition, peptide drugs are described from different perspectives by combining the information of the primary, secondary and tertiary structures. Three algorithms including support vector machine (SVM), K-nearest neighbor (KNN) and random forest (RF) are utilized to train the model. Then the support vector machine based on recursive feature elimination method (SVM-RFE) removes redundant features and identifies the key characteristics of different types of drugs. The added network features can more comprehensively describe peptide drugs, providing a theoretical basis for the analysis and design of new peptide drugs. The web sever of ACHP is freely available at http://118.178.58.31:9801/.