  In videos, the human's actions are of three-dimensional (3D) signals. These
videos investigate the spatiotemporal knowledge of human behavior. The
promising ability is investigated using 3D convolution neural networks (CNNs).
The 3D CNNs have not yet achieved high output for their well-established
two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional
Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN
from accomplishing remarkable evaluation. In this paper, we implement Hybrid
Deep Learning Architecture that combines STIP and 3D CNN features to enhance
the performance of 3D videos effectively. After implementation, the more
detailed and deeper charting for training in each circle of space-time fusion.
The training model further enhances the results after handling complicated
evaluations of models. The video classification model is used in this
implemented model. Intelligent 3D Network Protocol for Multimedia Data
Classification using Deep Learning is introduced to further understand
spacetime association in human endeavors. In the implementation of the result,
the well-known dataset, i.e., UCF101 to, evaluates the performance of the
proposed hybrid technique. The results beat the proposed hybrid technique that
substantially beats the initial 3D CNNs. The results are compared with
state-of-the-art frameworks from literature for action recognition on UCF101
with an accuracy of 95%.
