Word2Vec is a neural network-based technique that transforms words into continuous vector representations, capturing semantic relationships between words through their context in large corpora. By using shallow, two-layer neural networks, it efficiently learns word embeddings that can be used for various natural language processing tasks, enhancing the understanding of language semantics.