Application of Big Data Analysis Algorithms in Safety Risk Assessment of Intelligent Building Decoration Construction

In view of the problem that safety risks in the construction process of intelligent building decoration are difficult to accurately predict and respond to, this paper proposes a construction safety risk assessment method based on support vector machine (SVM). First, data is automatically collected and processed from various sources such as construction records and design drawings. On this basis, various factors in the construction process are analyzed to extract key features closely related to construction safety risk assessment. Then the algorithm is used for data mining and classification. Risk assessment is performed based on the trained SVM classification model. The average prediction accuracy of the SVM model is as high as 98.925%, which is significantly better than the rule-based method in terms of prediction time. This paper realizes the rapid processing and analysis of real-time construction data, providing decision support for engineering managers.