The conventional quantitative regression analysis method for processing millions of grid aerospace big data is not suitable for complex flow field environments. Under this background,  machine learning models may be a promising toll that can be used to solve this problem. However, the existing machine learning models may not simultaneously have sufficient prediction accuracy, model interpretability, and big data processing capabilities. To solve this challenge, a new deep decision tree model is proposed. Based on the stacked deep forest model, the hidden features are extracted and utilized by adaptive multi-granularity scanning and self-growing cascade forests. Using aerospace big data for experiments, the results show that the proposed model is superior to the random forest, extreme gradient boosting, and light gradient boosting machine models in terms of prediction accuracy, generalization performance, and core function gain.