Suicide is a serious public health problem in today’s society. It is of great social significance to conduct in-depth research on suicide prevention. This work studies the suicide risk assessment method based on Microblog text. According to Microblog text features, in order to solve the bottleneck problem of the current neural network single structure in the prediction accuracy improvement, this study proposes a hybrid architecture neural network model nC-BiLSTM and applies it to the Microblog text suicide risk identification. The model extracts local feature information by using multiple convolutional layers of different convolution kernels, and extracts contextual semantic feature information of sentences by using Bidirectional Long Short-Term Memory (BiLSTM) network layer. The experimental results show that the recognition accuracy, recall rate, and F value of the nC-BiLSTM model are better than other models. The results of this study can be applied to the early intervention of suicide prevention.