Speech emotion recognition has been widely used in multi-scenario intelligent systems in recent years， and it also provides the possibility to realize intelligent analysis of teaching behaviors in smart classroom environments. Classroom speech emotion recognition technology can be used to automatically recognize the emotional states of teachers and students during classroom teaching， help teachers understand their own teaching styles and grasp students’ classroom learning status in a timely manner， thereby achieving the purpose of precise teaching. For the classroom speech emotion recognition task， firstly， classroom teaching videos were collected from primary and secondary schools， the audio was extracted， and manually segmented and annotated to construct a primary and secondary school teaching speech emotion corpus containing six emotion categories. Secondly， based on the Temporal Convolutional Network （TCN） and cross-gated mechanism， dual temporal convolution channels were designed to extract multi-scale cross-fusion features. Finally， a dynamic weight fusion strategy was adopted to adjust the contributions of features at different scales， reduce the interference of non-important features on the recognition results， and further enhance the representation and learning ability of the model. Experimental results show that the proposed method is superior to advanced models such as TIM-Net （Temporal-aware bI-direction Multi-scale Network）， GM-TCNet （Gated Multi-scale Temporal Convolutional Network）， and CTL-MTNet （CapsNet and Transfer Learning-based Mixed Task Net） on multiple public datasets， and its UAR （Unweighted Average Recall） and WAR （Weighted Average Recall） reach 90.58% and 90.45% respectively in real classroom speech emotion recognition task.

With the massive growth of data， how to store and use data has become a hot issue in academic research and industrial applications. As one of the methods to solve these problems， instance selection effectively reduces the difficulty of follow-up work by selecting representative instances from original data according to the established rules. Therefore， a voting instance selection algorithm based on learning to hash was proposed. Firstly， the Principal Component Analysis （PCA） method was used to map high-dimensional data to low-dimensional space. Secondly， the k-means algorithm was used to perform iterative operations by combining with the vector quantization method， and the hash codes of the cluster center were used to represent the data. After that， the classified data were randomly selected according to the proportion， and the final instances were selected by voting after several times independent running of the algorithm. Compared with the Compressed Nearest Neighbor （CNN） algorithm and the instance selection algorithm of linear complexity for big data named LSH-IS-F （Instance Selection algorithm by Hashing with two passes）， the proposed algorithm has the compression ratio improved by an average of 19%. The idea of the proposed algorithm is simple and easy to implement， and the algorithm can control the compression ratio automatically by adjusting the parameters. Experimental results on 7 datasets show that the proposed algorithm has a great advantage compared to random hashing in terms of compression ratio and running time with similar test accuracy.

The information transmission ability of command information system was analyzed from the angle of uncertainty. The command information system network was divided into physical layer and logical layer, and relationships between information transmission and the layers were expounded. The effective working probability of nodes and links, time delay, logical links and physical links were taken into account. The information quantity was used to measure the uncertainty of information transmission, and then the information transmission ability of command information system was educed. The experiment was designed using combat command relationship, and influences of these factors above on information transmission ability were reflected. The experimental results show that the evaluation method take the demand of connectivity, timeliness and correctness in information transmission into consideration.

An algorithm based on the key to hash the digital watermark embedding positions was presented. The Scrambling algorithm used shifting and shuffling to hash the watermark embedding positions many times. It is very simple and convenient to carry it out, and then the positions are stochastic and dispersive. The security of embedding positions relies on a key, which to a certain extent makes the result of scrambling perfectly, and increases the security of the system.