Traditional mechanical testing methods require the specimen to be machined into specific shapes and sizes, which can not meet the test requirements of specific application conditions. Indentation method enables in-situ testing and convenient sample preparation, while the local loading mode of indentation leads to the complexity of theoretical analysis and characterization for materials mechanical parameters acquisition. Combing the indentation response and neural network algorithm can provide a new way to effectively obtain material mechanical parameters. In this study, tensile and spherical indentation tests of Cu and Fe have been carried out by the Instron universal material testing machine. The features of obtained indentation load-depth curves were extracted as the data basis for subsequent studies. Based on the secondary development of Abaqus software, a series of indentation numerical simulations with different combinations of plastic parameters were performed to obtain the corresponding indentation load-depth curves used for neural network training. By comparing the different optimal parameter finding strategies, activation functions, and methods of initializing neural network parameters, the neural network structure with good learning effect was determined to effectively obtain metal plastic parameters. Combining the indentation load-depth curve features form indentation test and the trained neural network, the related plastic mechanical parameters of Cu and Fe were obtained. Through the comparison of corresponding plastic parameters values from neural network learning and tensile test characterization, effectiveness of the proposed method for obtaining metal plastic mechanical parameters based on neural network algorithm and spherical indentation load-depth curve was verified. This study provides a new method for effectively acquiring mechanical properties parameters of metals/alloys.