earticle

영어

Under the highly development of the information society, only by carrying out education reform can be cultivate more innovative talents. Because the traditional education idea has already taken root in the hearts of people, education informatization is a necessary way to change this kind of thought. The generation of cloud computing technology leads to the revolution of data processing technology. It can make use of a small amount of resources to effectively deal with the big data in the information system of educational institutions. Neural network is one of the important technologies of educational data mining in cloud computing environment. BP neural network is a typical multi-layer forward network, which is composed of input layer, hidden layer and output layer. It can be used to predict the data through the training model. In this paper, based on the characteristics of the distribution of education resources, we put forward the method to analyze big data of education by using Hadoop technology. This method uses the MapReduce programming model to manage the data, so as to improve the speed and efficiency of data analysis. Secondly, in Hadoop platform, this paper puts forward the method of parallel BP neural network in education data processing. The method consists of the following main steps: firstly, input data and set up a three layer parallel neural network. Secondly, according to the location of each node to block the data, and transfer M separate blocks to the Map function for processing. Thirdly, through the gradient descent method, the Map function finds the weight distribution of each block by iterative algorithm. Fourthly, we transfer the key-vlaue to the Reduce function, and update the statistics. Finally, repeat the update the calculation process of weight. After several iterations, the optimal solution of the objective function is found, and the weight distribution of the network is obtained. Finally, we simulate the parallel BP neural network algorithm based on education cloud platform, in order to prove that it is suitable for the prediction of learning achievement of the network teaching system.