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Aiming at the feature and tendency the complexity, diversity and malignity of various users in Internet of things, a predicting method for Internet of things safety based on Gibbs-LDA and least square SVM is proposed. Firstly, the communication time, address and content is regarded as the original multi-dimension communication sample, then based on the LDA model, the safety event is built as the theme to obtain the sample feature and the LDA model. The Gibbs algorithm is used to estimate the parameters of LDA model, therefore, the Internet of things safety multi-dimension model is built. Finally, the weight distribution of feature and safety event over the LDA feature space is obtained, and the least square SVM with the biggest weight is used as the final predicting result. The simulation experiment shows the method in this paper can realize the predicting for network security event for Internet of things with higher predicting accuracy, and compared with the other methods, it has higher predicting accuracy and less predicting time, therefore, it has big priority over the other methods.