Channel encoding plays a vital role in modern communication systems by maintaining data integrity and reducing the impact of noise. In this paper, we propose a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to classify various channel encoders. This approach aims to improve feature extraction and classification performance compared to traditional CNN architectures. In typical scenarios, receivers are aware of the encoder’s type and configuration. However, in non-cooperative environments such as military communications, surveillance, and cognitive radio systems, this information is often limited or unavailable. To address this, we explore a deep learning-based method to identify four types of encoders: block, convolutional, Bose–Chaudhuri–Hocquenghem (BCH), and polar encoders. By integrating CNN and LSTM layers, our proposed model achieves up to 98% classification accuracy and demonstrates strong generalization. Comparative analysis reveals that the hybrid model outperforms conventional CNN-based methods in terms of accuracy and robustness.