Analysis of Influencing Factors of CBOW Model in Natural Language Processing Based on Quantum Neural Network

To address the problems of the limited feature extraction capability and insufficient training efficiency of the traditional Continuous Bag-of-Words (CBOW) model in Natural Language Processing (NLP), the Quantum Neural Network-enhanced CBOW model (QNN-CBOW) integrates Quantum Neural Networks (QNN) with the CBOW model, effectively enhancing training performance. This work aims to systematically investigate the sensitivity and influence patterns of key factors (activation function type, number of quantum feature extraction layers, context window size, and quantum gate noise level) on model behavior under controlled small-scale simulation conditions. Comparative experiments are carried out using the control variable method to clarify the influence mechanism of each factor. This paper presents a NISQ-era proof-of-concept study, which provides a theoretical basis and practical reference for the fusion and optimization of quantum neural networks and traditional NLP models.