The growing popularity of art majors is overshadowed by the difficulty for art graduates to find a decent job. The teaching and research (T&R) performance of art majors should be evaluated comprehensively, such as to optimize resource allocation and improve teaching quality. Based on performance evaluation theory and input-output theory, this paper summarizes the features, problems and problem causes of performance evaluation for art majors. From the dimensions of teaching and research, a comprehensive index system was designed for T&R performance evaluation of art majors. On this basis, a performance evaluation model was constructed for art majors through principal component analysis (PCA). Finally, single-factor evaluation and comprehensive evaluation were conducted by our model on the T&R performance of the school of art design and school of industrial design in a university. The results show that: The performance evaluation of art majors is a small-scale yet complex and professional task, but the current evaluation methods lack sufficient attention, professionality, and targeted indices. The school of art design had a slightly higher ratio of excellent theses than the school of industrial design, reflecting the difference between the two schools in teaching results and quality. The school of industrial design invested much more than the school of art design. Both schools had improved the T&R outputs and comprehensive performance score since 2011, and the school of industrial design made the greater improvement in the comprehensive performance score. The research sheds new light on comprehensive evaluation of T&R performance in art majors.

At present, part of people's body is in the state of sub-health, and more people pay attention to physical exercise. Dance is a relatively simple and popular activity, it has been widely concerned. The traditional action recognition method is easily affected by the action speed, illumination, occlusion and complex background, which leads to the poor robustness of the recognition results. In order to solve the above problems, an improved residual dense neural network method is used to study the automatic recognition of dance action images. Firstly, based on the residual model, the features of dance action are extracted by using the convolution layer and pooling layer. Then, the exponential linear element (ELU) activation function, batch normalization (BN) and Dropout technology are used to improve and optimize the model to mitigate the gradient disappearance, prevent over-fitting, accelerate convergence and enhance the model generalization ability. Finally, the dense connection network (DenseNet) is introduced to make the extracted dance action features more rich and effective. Comparison experiments are carried out on two public databases and one self-built database. The results show that the recognition rate of the proposed method on three databases are 99.98, 97.95, and 0.97.96%, respectively. It can be seen that this new method can effectively improve the performance of dance action recognition.

This paper proposes a dance posture analysis method based on eigenvector matching, and sets up a motion capture system that calculates motion parameters by similarity matching of characteristic planes. Considering the complexity of dance learning, the proposed system was adopted to acquire various postures of dance learners, and contrast their dance postures against the standard ones. The proposed analysis method was verified through a contrastive experiment, shedding new light on the innovation of digital dance teaching.

In recent years, there are more and more intelligent machines in people's life, such as intelligent wristbands, sweeping robots, intelligent learning machines and so on, which can simply complete a single execution task. We want robots to be as emotional as humans. In this way, human-computer interaction can be more natural, smooth and intelligent. Therefore, emotion research has become a hot topic that researchers pay close attention to. In this paper, we propose a new dance emotion recognition based on global and local feature fusion method. If the single feature of audio is extracted, the global information of dance cannot be reflected. And the dimension of data features is very high. In this paper, an improved long and short-term memory (LSTM) method is used to extract global dance information. Linear prediction coefficient is used to extract local information. Considering the complementarity of different features, a global and local feature fusion method based on discriminant multi-canonical correlation analysis is proposed in this paper. Experimental results on public data sets show that the proposed method can effectively identify dance emotion compared with other state-of-the-art emotion recognition methods.

The classification accuracy of EEG signals based on traditional machine learning methods is low. Therefore, this paper proposes a new model for the feature extraction and recognition of dance motor imagery EEG, which makes full use of the advantage of anti-aliasing filter based on whale parameter optimization method. The anti-aliasing filter is used for preprocessing, and the filtered signal is extracted by two-dimensional empirical wavelet transform. The extracted feature is input to the robust support matrix machine to complete pattern recognition. In pattern recognition process, an improved whale algorithm is used to dynamically adjust the optimal parameters of individual subjects. Experiments are carried out on two public data sets to verify that anti-aliasing filter-based preprocessing can improve signal feature discrimination. The improved whale algorithm can find the optimal parameters of robust support matrix machine classification for individuals. This presented method can improve the recognition rate of dance motion image. Compared with other advanced methods, the proposed method requires less samples and computing resources, and it is suitable for the practical application of brain-computer interface.