To promote the application of artificial neural network technology in identifying the origin of gems, an artificial neural network model of semi-quantitative trace element content of bluish white nephrites obtained by laser-induced breakdown spectrometer was established. The element content data were obtained by laser-induced breakdown spectrometer in the uniform and clean parts of nephrites from six regions: Xinjiang, Guangxi, Jiangsu, Qinghai, Korea and Russia. After screening using data filtering principles and normalizing the data, the collinearity between data is discussed by factor analysis and linear regression, and the discriminant model of the artificial neural network is established. The results show that the VIF value of each selected variable is less than 5, so there is no obvious multicollinearity among the selected elements. The KMO value of factor analysis is less than 0.6, indicating that there is no obvious relationship between variables. Moreover, thet-SNE graph of nephrite is used to reduce and visualize the data. T-SEN graph shows that most of the data points are overlapped together, indicating that the data’s simple clustering and correlation analysis could not distinguish the origin. Therefore, the artificial neural network is selected for the identification analysis of the six origin data. After the iterative discrimination of the artificial neural network model, the accuracy of the model for the identification of the blue and white nephrite from six producing areas is up to 0.933, and the nephrite from Korea has the highest data discrimination accuracy of 0.995 with an error of 0.028,while nephrite from Qinghai has the lowest data discrimination accuracy of 0.803 with an error of 0.090. In conclusion, a laser-induced breakdown spectrometer combined with the artificial neural network has great potential in applying gem origin tracing.