In relation extraction tasks， distant supervision is a common method for automatic data labeling. However， this method will introduce a large amount of noisy data， which affects the performance of the model. In order to solve the problem of noisy data， a relation extraction method based on negative training and transfer learning was proposed. Firstly， a noisy data recognition model was trained through negative training method. Then， the noisy data were filtered and relabeled according to the predicted probability value of the sample， Finally， a transfer learning method was used to solve the domain shift problem existing in distant supervision tasks， and the precision and recall of the model were further improved. Based on Thangka culture， a relation extraction dataset with national characteristics was constructed. Experimental results show that the F1 score of the proposed method reaches 91.67%， which is 3.95 percentage points higher than that of SENT （Sentence level distant relation Extraction via Negative Training） method， and is much higher than those of the relation extraction methods based on BERT （Bidirectional Encoder Representations from Transformers）， BiLSTM+ATT（Bi-directional Long Short-Term Memory and Attention）， and PCNN （Piecewise Convolutional Neural Network）.

In order to solve the occlusion problem of student expression recognition in complex classroom scenes， and give full play to the advantages of deep learning in the application of intelligent teaching evaluation，a student expression recognition model and an intelligent teaching evaluation algorithm based on deep attention network in classroom teaching videos were proposed. A video library， an expression library and a behavior library for classroom teaching were constructed， then， multi-channel facial images were generated by cropping and occlusion strategies. A multi-channel deep attention network was built and self-attention mechanism was used to assign different weights to multiple channel networks. The weight distribution of each channel was restricted by a constrained loss function， then the global feature of the facial image was expressed as the quotient of the sum of the product of the feature times its attention weight of each channel divided by the sum of the attention weights of all channels. Based on the learned global facial feature， the student expressions in classroom were classified， and the student facial expression recognition under occlusion was realized. An intelligent teaching evaluation algorithm that integrates the student facial expressions and behavior states in classroom was proposed， which realized the recognition of student facial expressions and intelligent teaching evaluation in classroom teaching videos. By making experimental comparison and analysis on the public dataset FERplus and self-built classroom teaching video datasets， it is verified that the student facial expressions recognition model in classroom teaching videos achieves high accuracy of 87.34%， and the intelligent teaching evaluation algorithm that integrates the student facial expressions and behavior states in classroom achieves excellent performance on the classroom teaching video dataset.

In view of the problems that classroom teaching scene is obscured seriously and has numerous students， the current video action recognition algorithm is not suitable for classroom teaching scene， and there is no public dataset of student classroom action， a classroom teaching video library and a student classroom action library were constructed， and a real-time multi-person student classroom action recognition algorithm based on deep spatiotemporal residual convolution neural network was proposed. Firstly， combined with real-time object detection and tracking to get the real-time picture stream of each student， and then the deep spatiotemporal residual convolution neural network was used to learn the spatiotemporal characteristics of each student’s action， so as to realize the real-time recognition of classroom behavior for multiple students in classroom teaching scenes. In addition， an intelligent teaching evaluation model was constructed， and an intelligent teaching evaluation system based on the recognition of students’ classroom actions was designed and implemented， which can help improve the teaching quality and realize the intelligent education. By making experimental comparison and analysis on the classroom teaching video dataset， it is verified that the proposed real-time classroom action recognition model for multiple students in classroom teaching video can achieve high accuracy of 88.5%， and the intelligent teaching evaluation system based on classroom action recognition has also achieved good results in classroom teaching video dataset.

For the vital arc problem of maximum dynamic flow in time-capacitated network, the classic Ford-Fulkerson maximum dynamic flow algorithm was analyzed and simplified. Thus an improved algorithm based on minimum cost augmenting path to find the vital arc of the maximum dynamic flow was proposed. The shared minimum augmenting paths were retained when computing maximum dynamic flow in new network and the unnecessary computation was removed in the algorithm. Finally, the improved algorithm was compared with the original algorithm and natural algorithm. The numerical analysis shows that the improved algorithm is more efficient than the natural algorithm

For the problems of long runtime, ignoring the difference between classes of sample, the paper put forward an algorithm called Global Weighted Sparse Locality Preserving Projection (GWSLPP) based on Sparse Preserving Projection (SPP). The algorithm made sample have good identification ability while maintaining the sparse reconstruction relations of the samples. The algorithm processed the samples though sparse reconstruction, then made the sample on the projection and maximized the divergence between classes of sample. It got the projection and classified the sample at last. The algorithm made the experiments on FERET face database and YALE face database. The experimental results show the GWSLPP algorithm is superior to the Locality Preserving Projection (LPP), SPP and FisherFace algorithm in both execution time and recognition rate. The execution time is only 25s and the recognition rate can reach more than 95%. The experimental data prove the effectiveness of the algorithm.

Concerning a kind of continuous-time capacitated network with limits on nodes process rate, a shortest dynamic time flow was proposed and its corresponding linear programming form was also given. Based on the inner relationship of the above-mentioned network and the classical continuous-time capacitated network, efficient algorithms in terms of the thought of maximal-received flow and returning flow were designed to precisely solve the shortest dynamic time flow issue in those two kinds of network respectively. Afterwards, the algorithms were proved to be correct and their complexities were also concluded to be small. Finally, an example was used to demonstrate the execution of the algorithm.

This paper proposed a new Multi-Space Multi-Resolutin Markov Random Field Model (MS-MRMRF). Concerning the inadequate description of the color images in a single RGB space, the proposed model firstly transformed images from the RGB color space to the HSV color space and combined these two color spaces as a multi-space feature; then a new multi-resolution Markov model was designed to segment the image based on the multi-space feature, which estimated the parameters by fuzzy theory. The experiments of the color images demonstrate that the segmentation results of MS-MRMRF model have a higher segmentation accuracy compared with the segmentation results of multi-resolution MRF with a single RGB space.

The control and treatment of water pollution is an important issue in China. To meet the lack of remote monitoring of water, the ship video transmission and protection system based on 3G network was proposed. The structure of the system was described, and the characteristics of the 3G network video transmission were analyzed. The achievement of smooth real-time video transmission was based on 3G network, simple reliable user datagram protocol, H.264 video codec, and Quality of Service (QoS) control. The results show that the system is effective, and it can be applied to real-time video surveillance of water.

To improve the efficiency of information retrieval, a Path-based OWL Storage (POS) model was proposed. In addition, the structure of the POS system for the translation and storage of OWL data was illustrated. A data schema of inputted OWL and a data graph with hierarchical structural information between classes or properties were analyzed by POS system. Also, paths from the root class or property to all classes or properties were extracted via a Depth-First-Search (DFS) method. The extracted hierarchical structural information was stored in a path attribute in the relational database tables. Compared with the traditional method, the processing time for ontology query and update in the experiment has a feasible improvement.