The continuous evolution and complexity of cyber threats make the identification and classification of data assets in the cybersecurity space crucial. This study uses deep learning techniques, especially neural network models, to conduct in-depth analysis and modeling of network threat behavior, aiming to improve the security and reliability of data assets. Firstly, this article provides an overview of the current application status and potential value of neural networks in the field of network security. Aiming at the shortcomings of existing neural networks in data classification accuracy and computational efficiency, a hybrid neural network model based on incremental learning strategy (combining CNN and RNN) is proposed to improve the recognition and classification efficiency of network security spatial data assets. The experimental results show that the proposed improvement method has significantly improved the accuracy of data classification and recognition, and has important practical application value in the field of network security.