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Edge computing, as a new computing paradigm that solves the pressure of central nodes and is closer to terminal devices and data sources, fulfils the needs of Calculate subsidence, but also brings security crisis. Among them, DDos are the ones that most threaten the security of edge computing and cause enormous economic losses and security accidents. In edge computing environment, traditional defense methods are difficult to apply due to limited computing power and storage space. Therefore, this paper proposes a lightweight DDoS detection framework based on deep learning for edge computing environment. In this paper, cic-ddos-2019 data set is used to simulate the network traffic attacked by DDoS in the edge computing environment. The adaptive preprocessing technology and similarity label fusion are carried out for the data set. The smote algorithm is used to solve the problem of data set category imbalance, and a lightweight model is constructed by using one-dimensional convolution technology, bilstm technology and model pruning technology. As a result, it achieves 96.8% accuracy in 8-class and 99.8% accuracy in 2-class experiments for DDos attack categories.