Survival analysis has currently become a hot topic because it has been proven to be useful for understanding the relationships between patients’ covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. In this study, we study survival analysis of breast cancer patient with gene methylation data and clinical data. We propose a novel method for survival prediction by bidirectional LSTM network and ordinal Cox model. First, gene methylation expression data and clinical data are merged and filtered to keep matching. To reduce the gene expression feature dimension, weight gene co-expression network analysis (WGCNA) algorithm is used to obtain the cluster eigengenes. Then, the eigengenes will be input features for machine learning network. We build a cox proportional hazards model for survival analysis and use LSTM method to predict patient survival risk. We use the leave_one_out method for cross validation, and use the concordance index (C-index) to evaluate the prediction performance. Stringent cross-validation tests on the benchmark dataset demonstrated the efficacy of the proposed method, which achieves very competitive performances to existing state-of-the-art methods.