In view of the problem that 3D-CNN can better extract the spatio-temporal features in video, but it requires a high amount of computation and memory, this paper designs an efficient 3D convolutional block to replace the 3´3´3 convolutional layer with a high amount of computation, and then proposes a 3D-efficient dense residual networks (3D-EDRNs) integrating 3D convolutional blocks for human action recognition. The efficient 3D convolutional block is composed of 1´3´3 convolutional layers for obtaining spatial features of video and 3´1´1 convolutional layers for obtaining temporal features of video. The spatial dimension convolution results are directly used as the input of time dimension convolution, which is helpful to retain the original information with abundant spatio-temporal characteristics. According to the residual network, the information flow can be transmitted from the shallow layer to the deeper layer. The dense network can apply the extended repetition features to the entire network. 3D-EDRNs is designed as a combination of a small dense connection network and a residual structure, which is used to extract the spatial-temporal features of video. The new dense residual structure extends the original dense residual structure from 2D to 3D, and integrates E3DB, which can accelerate the network training and improve the performance of the residual network. Input of the add layer is processed through the structural design of the DRB, which are all feature graphs of inactivated functions, thus, 3D-EDRNs can effectively obtain the information flow between convolutional layers, which is helpful for the network to extract the spatial-temporal features. The concatenate layer can fully integrate the shallow and high level features obtained by the network. 3D-EDRNs extracts the variable and complex spatio-temporal features of video, and the information flow between convolutional layers can also be transmitted to each layer smoothly, thus improving the utilization rate of network parameters and avoiding the problem of parameter expansion of common neural networks. Efficient 3D convolutional blocks are combined in multiple locations of dense residual network, which not only takes advantage of easy optimization of residual blocks and feature reuse of dense connected network, but also can shorten the training time and improve the efficiency and performance of spatial-temporal feature extraction of the network. In the classical data set UCF101, HMDB51 and the dynamic multi-view complicated 3D database of human activity (DMV action3D), it is verified that the 3D-EDRNs combined with 3D convolutional block can significantly reduce the complexity of the model, effectively improve the classification performance of the network, and have the advantages of less computational resource demand, small number of parameters and short training time.