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Human body pose recognition and modern intelligent engineering design have become an important research direction in the field of human-computer interaction. In the realization of more efficient and intelligent human posture recognition, using the density based application with noise of spatial clustering and random forest (Density-Based Spatial Clustering of Applications with Random Forest, DBSCAN-RF) classification trainer, the technical innovation and unique method combines the advantages of density clustering and random forest, can effectively deal with noisy data sets, and has high computational efficiency and scalability. Through experimental testing, the recognition recall rate of DBSCAN-RF algorithm reached the highest level of 98.64%, which increased by 6.37%, 4.28%, and 3.95% compared with the traditional RF algorithm, K-means-RF and Mean-shift-RF algorithm. Meanwhile, the recognition recall rate of the DBSCAN-RF algorithm reached 95.31% and 96.48% for falls and normal walking, respectively. Moreover, the test time of the DBSCAN-RF algorithm was all lower than 62ms. The practical application meets the application of modern intelligent body posture recognition engineering and provides reliable technical support for modern intelligent body posture recognition.