Coiling temperature control precision is the main elements influencing the presentation of strip steel items, and further developing curling temperature control exactness and guaranteeing winding hit rate is a main point of contention in the field of hot rolling. To resolve the issue of low hit pace of individual steel grades in the current coiling temperature setting model of a steel mill, a new modeling idea based on Gray Wolf Optimized Extreme Learning Machine is proposed combining data mining and field master insight, and Henon mapping, small-hole imaging strategy and weight factor strategy are introduced to improve the Gray Wolf algorithm, and a hot-rolled based on Improved Gray Wolf Optimized Extreme Learning Machine (IGWO-ELM) is established. strip coiling temperature prediction model based on the Improved Gray Wolf Optimized Extreme Learning Machine (IGWO-ELM) and contrasted and ELM model, GA-ELM model and GWO-ELM model. The model results show that the established IGWO-ELM model has a hit rate of 91.1% for predicting the coiling temperature within ±3°C and 96.7% for predicting the coiling temperature within ±4°C, both of which are better than the comparison models and have a wide range of pragmatic application prospects.