Deep Learning Technology in Data Information System

With the development of data processing technology, data processing and analysis have become a problem that must be faced today. The deep learning technology is composed of a feedforward neural network, and the model nodes form a layer of association relationships. This paper studies the processing efficiency, accuracy and error of the network model based on deep learning technology. The segmentation statistics of language data by different models are studied separately, and the delay and total processing time of a large number of file data processing in the information system are studied. As the number of deep learning model node nodes increases, the data processing time will be greatly reduced. When processing the same amount of data, small data files will consume more time. With the expansion of the cluster, task allocation and data transmission will consume part of the system resources and time, but as the scale of data increases, the system processing capabilities gradually appear, and the average processing delay gradually decreases.