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In order to solve the problem of sparse data and scalability, a new recommendation algorithm for device based on large data technology is proposed. Large-scale multi-disciplinary devices resources are used as recommendation resources, and their processing is completed by open source MapReduce technology. The open source Waikato intelligent analysis environment and Hadoop large data processing platform are combined to provide a basis for the recommendation algorithm of device. K-means algorithm is used to device fault. The similarity is calculated by weighting the similarity of the scoring matrix and the feature of the device. The first results with the largest similarity are taken as the nearest neighbors. In order to ensure the accuracy of recommendation, singular value decomposition (SVD) algorithm is used to fill in the missing values of each row through the average of each fault score matrix, and the filled score matrix is obtained to recommend control strategies to the target devices. It is verified that the proposed algorithm has high accuracy.