With the rapid development of modern technology, the data center has become the IT infrastructure of the information society, storing and managing a large amount of key data. At present, the management of data centers mostly relies on experienced professional operation and maintenance personnel to use computers to automatically monitor equipment room equipment indicators and make multiple inspections of equipment, which is time-consuming and tedious. Deep learning and artificial intelligence technologies are currently attracting more and more attention and have achieved many successful applications in the Internet and industrial fields. This study designs a Gated Recurrent Unit (GRU) based deep learning framework to automatically diagnose equipment failures in cloud data center equipment rooms and combines timing information to predict future states based on past equipment operating status information. Series data are split into fixed time windows as input to the bidirectional GRU layer which makes the network learn the time dependency relationship in data points. Besides, we add an attention layer and embedding layer after the output of GRU unit, to help the neural network learning more efficient features for prediction task and further dimension reduction. At last, multi-layer perception is used to classify the data. Experimental results based on real data sets show that proposed neural network framework based on GRU can accurately detect cloud data center faults compared with LSTM, SVM and KNN.