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Research on Big Data Classification Technology Based on Deep Learning

Based on the current digital industry background, the production processes become centralized and closely related to each other. If industrial production fails, it will bring great damage to it, which makes it more and more important to accurately identify the status of industrial equipment and repair it in real time by collecting time series data with industrial sensors. However, due to the large number, variety and high-frequency sampling of sensors, industrial big data has certain complexity, i.e. high spatial dimension, complex logical relationship, changeable rules, large amount of data and so on. At present, end-to-end algorithms of deep learning are widely used in many fields. From the perspective of application of deep learning, this paper studies its application in industrial big data time series classification, and analyzes the characteristics of industrial data and the challenges of industrial time series classification from three aspects: accurate classification, efficient classification and incremental learning.