In the Internet era, network malicious intrusion behaviors occur frequently and network intrusion detection is increasingly in demand. Addressing the challenges of high-dimensional data, nonlinearity and noisy network traffic data in network intrusion detection, a net-work intrusion detection model is proposed in this paper. Firstly, a hybrid multi-model feature selection and kernel-based dimensionality reduction algorithm is proposed to map high-dimensional features to low-dimensional space to achieve feature dimensionality reduction and enhance nonlinear differentiability. Then the semantic feature mapping is introduced to convert the low-dimensional features into color images which represent distinct data characteristic. For classifying these images, an integrated convolutional neural network is constructed. Moreover, sub-model fine-tuning is performed through transfer learning and weights are assigned to improve the performance of multi-classification detection. Experiments on the UNSW-NB15 and CICIDS 2017 datasets show that the proposed model achieves accuracies of 99.99% and 99.96%. The F1-scores of 99.98% and 99.91% are achieved respectively.