For improving the predictive ability of existing models predicting the compaction density of asphalt pavement, the test site is created in the upper layer of the Xiang’an Airport highway project in Xiamen. The CCV, DMV and VCV, which represent the change of harmonic ratio, energy, and stiffness respectively, and temperature are chosen as indicators. The isolation forest algorithm is used to detect outliers in indicators. The model for assessing density is established based on the partial least squares regression, and the fitting ability of it and common multiple linear regression methods are compared. The results show that the isolation forest can effectively recognize outliers of high-dimensional data, covering the shortage that traditional methods can only process one-dimensional data. There are different degrees of positive correlation between temperature, other indicators, and asphalt pavement density. The multiple regression model, which is established by CCV, DMV, and VCV, obtains better fitting ability than unitary regression methods, proving the feasibility of multiple indicators. The partial least squares regression can restrain the adverse impact caused by the approximate collinearity between independent variables, correct the incorrect weight of temperature and improve the fitting degree compared with the common multiple linear regression methods. The final determination coefficient of the model on the training set is 0.83, and on the test set is 0.81.