Continuous Bag of Words (CBOW) is a neural network model used in natural language processing to predict a target word based on its surrounding context words, effectively capturing word semantics by leveraging context information. It is part of the Word2Vec framework, which transforms words into continuous vector representations, facilitating tasks like word similarity and clustering.