N4-acetylcytidine (ac4C) is a crucial RNA modification associated with mRNA stability and translational efficiency. Accurate identification of ac4C sites is essential for understanding their regulatory functions. However, experimental detection remains expensive and labor-intensive. At the same time, existing computational models suffer from limited generalization and insufficient feature discrimination, especially in distinguishing subtle nucleotide patterns. In this work, we propose a deep learning model named SNN-ac4C, which is based on a contrastive learning-based neural network. The model integrates a dual-path structure that combines BiLSTM and Multi-Head Self-Attention (MHSA) for capturing long-range dependencies and global context, while using CNN to extract local biological sequence features. The contrastive learning module further enhances the discriminative ability of ac4C and Non-ac4C sites by increasing the separation between positive and negative samples. Experiments on the test set confirm the effectiveness of SNN-ac4C, which achieves an accuracy (ACC) of 84.60% and a Matthews Correlation Coefficient (MCC) of 0.6934. Compared with NBCR-ac4C, the current state-of-the-art model, SNN-ac4C improves ACC and MCC by 1.09% and 0.0228, respectively. The source code and relevant supplementary are publicly available at https://github.com/2103374200/SNN.