Protein structure identification is a complex and computationally intensive problem. Predicting protein structure classes is a crucial step in this process. This study investigates the application of hybrid quantum neural networks (HQNNs) for classifying protein sequences, an area where traditional computational methods often struggle due to the complex nature of biological data. The integration of quantum computing elements within neural networks presents a novel approach to enhancing classification accuracy by leveraging the unique capabilities of qubits, such as superposition and entanglement. In this study, various feature extraction techniques, including Word2Vec, FastText, and Skip-Gram models, are used to convert protein sequences into a format suitable for processing using both classical and quantum-enhanced machine learning models. The methodology involves a comparative analysis between classical neural networks and hybrid quantum neural network (HQNN), where quantum circuits are incorporated as layers within traditional network architectures. The performance of these hybrid models is assessed against purely classical networks, focusing on their ability to handle high-dimensional, complex datasets typically encountered in protein sequences. The classification accuracy for both neural networks was comparable, with the quantum hybrid model performing slightly lower. The classical neural network achieved a highest accuracy of 88.23%, while the hybrid quantum neural network reached an accuracy of 84.85%.