Vehicle loads and environmental differences are the key technical factors in the model construction of concrete pavement damage prediction. According to the data of the 168-month actual number of actions of different vehicle axle types, average temperature, average wind speed, rainfall, snowfall and days below 0 ℃ collected from the Mudanjiang-provincial section of the He-da highway in China, the broken slab ratio of cement concrete (DBL) was calculated. Cracking rate(CRK) and environmental factor(SF) were introduced into the model. This paper uses SPSS analysis method to carry out correlation analysis and partial correlation analysis by introducing SF to the model of DBL and CRK, so that the concrete pavement damage prediction model in seasonally frozen regions can be constructed and tested. Results show that CRK and SF both have positive linear relationship with DBL; Concrete pavement damage in seasonally frozen regions can be predicted by analyzing parameters like actual number of actions of different vehicle axle types, road service time and freezing index, etc. No multiple collinearity exists in the parameters of the model and the construction of model for concrete pavement damage prediction in seasonally frozen regions is of great theoretical significance for timely and effective pavement maintenance. The model has achieved good results in damage prediction accuracy and efficiency.