At present, the construction of college English network resource database in domestic universities is still in its initial stage in practice, and it is almost a blank in theoretical research. The construction of college English teaching resource database is of great strategic significance for promoting the construction of college English subject resources and improving the talent training mode. In this regard, the purpose of this paper is to study the construction of college English teaching resource database under the background of big data. This paper first discusses the current situation of college English teaching resources construction and resources in China. This paper summarizes the main problems existing in contemporary Chinese college English teaching resources database. According to the previous investigation and research results, and combining with big data technology, this paper constructs a new college English teaching resource library. The new college English teaching resource library puts the feelings of college students at the main position, and its teaching resources are more inclined to the subjective initiative and communication of college students. The resource library also uses the storage architecture of data warehouse, combined with big data and other related technologies, to realize the storage and update of various English teaching resources, and meet the needs of effective management of teaching resources in colleges and universities. Finally, this paper compares the old and new college English teaching resources. The experimental results show that the college English teaching resource database constructed in this paper is more recognized and liked by students, and the degree of students’ liking for English has increased by about 55%, which plays a significant role in improving students’ English and provides an important reference for the construction of college English teaching resource database.

Teaching quality is the fundamental guarantee for colleges and universities(CAU) to achieve their educational goals, and is the basis for training talents. In recent years, an important research field of big data is the research of Data mining(DM). The use of DM algorithms can find out the key factors affecting teaching quality, provide a strong basis for teaching arrangements and teaching management in universities, and then improve the teaching level of universities. The purpose of this article is to study the evaluation system of college English teaching(ET) based on big data. Based on the idea of DM, this paper designs and implements a college ET quality evaluation system(ETQES), and applies the system to the college ET quality monitoring system, and obtains a large amount of evaluation data. This paper extracts relevant data from the school’s ETQES, teacher management system, comprehensive educational administration and other system databases, and builds a data warehouse through data preprocessing. This paper implements the DM module of the ETQES to mine the data in the data warehouse. Through the analysis of these rules, the factors affecting the evaluation results of ET quality are obtained. This paper establishes a database suitable for mining association rules. Through the analysis of association rules, find out which key factors can affect the quality of ET, so as to provide a strong basis for ET decision-making and management. According to the experimental results, it can be concluded that the 4555-year-old teacher gets a higher student evaluation score, indicating that teachers of this age group are more popular with students.

A neural network model and English teaching evaluation of university with end-user computing are the focus of this paper. The main research contributions are as follows: (1) Propose an ADA-BP neural network. It applies the adaptive learning rate as well as the momentum term to promote the BP network. Experiments show that the model solves the problems of the existing methods and models such as difficulty in determining weights, prone to overfitting, slow convergence, and prone to local minimums, which verifies the effectiveness in evaluating college English teaching. (2) Propose the DA-SVR network. The model has multiple hidden layers and performs multiple feature conversions to optimize the network. The output layer of the model uses SVR as a predictor to achieve evaluation. (3) Deploy the designed evaluation network on the server, and end users can use the API interface to directly access and test the model functions. Massive experiments have verified the effectiveness of the designed method.

Abstract. In this paper, taking quadrotor as the research object, a kind of airborne vision system is presented to detect and track the moving target. Lower cost and better performance are the remarkable characteristics of the system. Firstly, the overall scheme of the detection and tracking of quadrotor is designed. Secondly, a new image processing algorithm based on HSV color space is proposed. Finally, the algorithm is applied to the actual flight of the quadrotor to verify its feasibility.

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