Concerning the problem that detection performance and generalization performance of the classification algorithm model in fake news content detection cannot be taken into account at the same time, a model based on feature aggregation was proposed, namely CCNN (Center-Cluster-Neural-Network). Firstly, the global temporal features of the text were extracted by bi-directional long and short term recurrent neural network, and the word or phrase features in the range of window were extracted by Convolutional Neural Network (CNN). Secondly, the feature aggregation layer based on dual center loss training was selected after the CNN pooling layer. Finally, the feature data of Bi-directional Long-Short Term Memory (Bi-LSTM) and CNN were stitched into a vector in the depth direction and provided to the fully connected layer. And the final classification result was output by the model trained by uniform loss function (uniform-sigmod). Experimental results show that the proposed model has an F1 value of 80.5%, the difference between training and validation sets is 1.3%. Compared with the traditional models such as Support Vector Machines (SVM), Naïve Bayes (NB) and Random Forest (RF), the proposed model has the F1 value increased by 9%-14%; compared with neural network models such as Long Short Term Memory (LSTM) and FastText, the proposed model has the generalization performance increased by 1.3%-2.5%. It can be seen that the proposed algorithm can improve the classification performance while ensuring a certain generalization ability, so the overall performance is enhanced.