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The provided model highlights the semantic significance of mixed word associations in the input text, simultaneously reducing the reliance on information derived solely from dictionary statistics. We combine the correlation coefficient with the attention mechanism to establish the correlation coefficient between words. The quantum-like sentence representation based on pure state density matrix is extended to the projection operator of mixed states. Combining the acquisition of maximum in convex optimization, a constraint functions-based quantum-like text representation pruning model is established to reduce redundant information caused by dimensional expansion of tensor operations. 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