Action recognition is a basic and challenging task in the field of computer vision. In this paper, a deep learning action recognition method based on attention mechanism is proposed and successfully applied to several public data sets, with outstanding performance. Firstly, the video frames are sampled based on the improved sampling algorithm, and the video data enhancement algorithm is proposed to preprocess the original data, which will reduce the overfitting probability of the recognition model and reduce the white noise of the data. Then, feature selection is carried out through attention-based residual network. Finally, we completed the action classification by LSTM model and softmax. In addition, a series of ablation experiments were designed to verify the validity of the proposed model. The results indicate that compared with the traditional action recognition model, the proposed method can effectively extract key features, reduce the overfitting caused by a small number of samples, reduce the interference of redundant information through the screening of low-information video frames, and complete the action recognition accurately, quickly, and efficiently.

Color itself is a beautiful and wonderful existence. Chinese traditional colors contain Chinese aesthetic taste and ancient cultural precipitation. It is said that color is a way to understand the world, while traditional Chinese color is integrated into life and caring for the soul [15]. With the continuous development of science and technology, computers are widely used in various fields, and intelligent image color processing technology is an independent theoretical and technical field, but it is also an extremely important technical support for machine perspective graphics processing. In this paper, combined with intelligent image color processing technology, the color teaching of Chinese painting is studied, and based on the wavelet variant, the best blur system parameters are used to obtain high-quality images using BFPSO, PSO, and BFO learning mechanisms to form suitable coding. Through the experiment, the color of Chinese painting is tested and verified by intelligent image color processing technology, and through the experimental results, it can be seen that the accuracy rate and recall rate after intelligent image processing technology are close to 1, indicating that, after optimizing with BFPSO algorithm, the optimal solution is given priority to a certain extent. Therefore, the use of intelligent image color processing technology has further improved the color teaching of Chinese painting.

With the rapid rise in the number of people buying houses, the demand for interior space design has also increased accordingly. The diversification of existing room types and the diversity of the public’s perception of fashion make interior designers in short supply. The future of computer science and technology in the field of automatic design of indoor areas will be immeasurable. This paper proposes an automatic layout method for spatial area design based on convolutional neural networks (CNN). CNN methods are a fast and efficient method. By mimicking the designer’s design process, it proposes a two-stage algorithm that defines the room first and the wall later, and the algorithm also provides a large-scale dataset called RPLAN that contains more than 80,000 interior layout plans from real residential buildings. Starting from the prediction living room, the automatic layout of the indoor areas is completed by iteration. A large number of empirical results show that the interior area design effect of this method is comparable to the interior design floor plan of professional designers.