The entity relation extraction model based on neural networks has been proven effective, but a single neural network model is unstable because it can yield various results with different inputs. Therefore, this study proposes a method to integrate multiple single models into a comprehensive one using the idea of ensemble learning. Specifically, this method integrates Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) into a comprehensive model through MultiLayer Perceptron (MLP), which cannot only fully take advantage of the two single models, but also make use of the self-learning ability and automatic weight allocation of MLP. This study obtains F1 of 87.7% on the SemEval 2010 Task 8 dataset, which is better than other mainstream entity relation extraction models.