Currently

action recognition methods can hardly carry out feature analysis

behavior classification

and action completion

and are incapable of accurately identifying the complete behavioral action sequence of intelligent agent for the discontinuous and incomplete motion capture

behavioral data missing or even broken in the time dimension

which are resulted from sensor device not being continuous coverage caused by the monitoring blind area. In this regard

we put forward a method of action recognition based on deep learning and artificial intelligence planning. Firstly

a deep learning network is constructed

by which the image is classified and trained using DRN(Deep Residual Network).After that

the extraction depth information of image frame feature for recurrent neural network is trained to enhance the classification effect. Secondly

the STRIPS (Stanford Research Institute Problem Solver) planning model is used to extract the image feature of deep learning

transforming into the description document for domain model

which facilitates deriving the optimal planning solution by means of forward state-space search planner. In the experiment

we exhibit that our method outperforms baselines in the public datasets

e.g.

DCIGN(Deep Convolutional Inverse Graphics Networks)

GAN(Generative Adversarial Networks)

DBN(Deep Belief Networks)

and SVM(Support Vector Machine).