Objective: With the rapid development of network technology, cyberattacks have become increasingly severe, threatening the stability of cyberspace. Network security situation assessment (NSSA) has become a critical technology for building proactive defense systems by integrating multisource data to deliver comprehensive and dynamic evaluations of network states. Traditional rule-based methods and early learning-based models often lack sufficient granularity in feature extraction, struggling to capture long-range temporal dependencies, thereby limiting their effectiveness in detecting complex and diverse attack patterns. To address these limitations, this study proposes a novel evaluation framework that integrates a parallel feature extraction network (PFEN) and a multiscale temporal convolutional network (MsTCN) to enhance fine-grained feature extraction and long-term dependency modeling for network traffic data. Methods: The proposed PFEN-MsTCN model introduces two major technical contributions. First, the PFEN is tailored for sequential traffic data by modifying the conventional Inception module, replacing two-dimensional convolutions with one-dimensional convolutions to extract temporal features along the sequence axis. The multibranch structure is optimized into cascaded subnetworks to capture local and contextual temporal features. The integration of convolution, batch normalization, and ReLU activation enhances nonlinearity and robustness, effectively reducing computational complexity while maintaining feature quality. Second, MsTCN is improved by introducing a multikernel branching structure and a hierarchical dilation rate to dynamically capture multiscale temporal features. A dynamic parameter matching mechanism and Chomp1D layer ensure multibranch output alignment, preventing dimensional mismatches during feature fusion. This design enables the simultaneous detection of short-term bursts and long-range dependencies. Finally, the strengths of PFEN in local feature extraction and MsTCN in sequence modeling are seamlessly integrated, creating a robust hybrid model. Results: Comprehensive experiments on the NSL-KDD and CIC-IDS2017 benchmark datasets involved preprocessing with normalization, one-hot encoding of categorical features, and removal of redundant or invalid features to ensure high-quality input. Experimental results demonstrate that the PFEN-MsTCN model consistently outperforms the baseline models, including PFEN-ABiGRU, SEAE-CNN-BiGRU-AM, CNN-TCN, and Inception1D-MsTCN. On the NSL-KDD dataset, the proposed model achieved an F1-score of 87.39%, surpassing competing methods by 2.54%-4.88%, while maintaining lower mean squared error and mean absolute error values. On the CIC-IDS2017 dataset, the proposed model achieved an outstanding F1-score of 99.87% with reduced prediction error, demonstrating superior adaptability to heterogeneous and imbalanced data. The visualization of situation values further verified that PFEN-MsTCN aligns more closely with the ground truth than competing models. Furthermore, the proposed evaluation index system, incorporating attack impact, probability, and frequency factors, enabled accurate quantification of security situation values and precise risk level classification. Conclusions: The PFEN-MsTCN fusion model effectively addresses the challenges of existing NSSA methods by enhancing feature extraction granularity and improving the capture of long-term temporal dependencies. By integrating multibranch one-dimensional convolutional feature extraction with hierarchical multiscale temporal convolution, the model achieves precise recognition of abnormal traffic behaviors and robust temporal dependency modeling. The experimental results validate the superior performance of the proposed model in terms of accuracy, robustness, and generalization across datasets, establishing its potential as a reliable tool for intelligent network security assessment. Future research will focus on improving the recognition accuracy for small-sample attack types in imbalanced datasets and extending the framework to real-time and large-scale deployment scenarios, further enhancing its applicability in practical cyberspace defense systems.