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Background: The quality of construction is crucial in evaluating steel structure. However, traditional quality control methods for steel structure construction have been criticized for their lack of intelligence, resulting in a heavier reliance on manual experience and post-construction inspections to address quality issues. This shortcoming makes quality management inefficient and labor-intensive. To address this issue, this paper proposes a smart quality control method based on digital twin technology. Methods: In this framework, data collection is used for subsequent quality control throughout the construction process. To improve pre-construction quality control, a mixed reality (MR) system is used to guide and train personnel. During the steel structure construction process, the Markov method is used to analyze and predict real-time data. Results: To test the effectiveness of the proposed method, ten sets of parallel tests were conducted to predict whether the bolt torque value was normal or not, resulting in an 80% accuracy rate. Conclusions: The proposed method for steel structure construction quality control was effectively certified, achieving active prevention and real-time control of quality problems and improving the overall intelligence level of quality control.