The major achievements of intelligent compaction measurement value （ICMV） are systematically summarized in this paper. The following contents of ICMV are discussed： development history， calculation mechanisms， advantages and disadvantages， filler applicability and influencing factors between ICMV and in-situ test values. The analysis indicates that there are the matching mechanisms between ICMV and subgrade materials and ICMV should be selected according to these mechanisms to improve the detection effect. During the fitting analysis， the types of subgrade ICMV and in-situ measurement values should be considered comprehensively to determine the influencing factors， so as to avoid the redundancy or the loss of regression features. Since some features have nonlinear effects on labels， it is not recommended to use linear models to predict the labels of intelligent compacted data. In view of the above problems， specific suggestions， including matching relationship between ICMV and materials， the importance of influence factors and the selection strategies of regression algorithms， are presented here in order to serve as references for the future research and application of ICMV.