Digital transformation in the textile industry requires significant capital investment, creating a conflict between high upfront costs and uncertain long-term returns. Traditional static evaluation methods often fail to accurately predict the economic feasibility of such complex projects under dynamic market conditions. This paper proposes a dynamic Cost-Benefit Assessment Model (CBAM) based on engineering economic analysis, specifically tailored for intelligent retrofitting projects in textile spinning enterprises. By integrating the Net Present Value (NPV) method with a dynamic Life Cycle Cost (LCC) analysis, the model quantifies critical variables including energy consumption per unit, labor reduction efficiency, and quality-induced price premiums. A sensitivity analysis is further introduced to evaluate project robustness against fluctuations in raw material prices and electricity rates. The model is validated through a case study of a 50,000-spindle technological upgrade project in a typical cotton spinning enterprise. The results indicate that the proposed model reduces the cost prediction error to within 4.2% compared to actual operational data and identifies the break-even point 14 months earlier than traditional linear projections. This study provides a scientific and quantitative decision-making tool for textile engineering management, ensuring the rationality and reliability of investment strategies in Industry 4.0 transformations.

engineering economics, cost-benefit analysis, textile manufacturing, intelligent retrofitting, sensitivity analysis