As one of the most pivotal resources, coal cannot be replaced. The evaluation of coal properties plays an essential role in the development. The calculation of coal properties based on laboratory analysis is inefficient and expensive. In this paper, the authors have resolved this problem by establishing the relationship between logging parameters and coal properties. Natural gamma, time difference, density and three-lateral resistivity are treated as input, and values of moisture, ash, volatile matter and fixed carbon are chosen as output. By using 73-layer logging data to train, the authors constructed a model based on GRNN and LS-SVM to calculate coal qualities. Through testing 19-layer data, the authors have reached the conclusion that these two methods can be well used in practice. The GRNN can calculate the content of moisture, ash, volatile matter and fixed carbon more accurately than LS-SVM, with its mean square error lower than 1%.