## 1 Introduction
They proposed a method to estimate and predict the distribution of the blast furnace charge, but this method could not give feedback on the change in the permeability index in the blast furnace during its actual operation. [PERSON] et al. [11] established a blast furnace permeability prediction model based on an improved multilayer limit-learning machine and wavelet transform and combined partial least squares with the multilayer limit-learning machine algorithm. They eliminated the multicollinearity of the last hidden layer of the multilayer limit-learning machine and improved the prediction accuracy of the model. However, at this stage, the research on blast furnace permeability based on big data analysis and intelligent algorithms is extremely limited and is still in the initial exploration stage. The aim of this study is to construct a data-driven prediction model to accurately predict the trend in the blast furnace permeability index, so as to ensure the stable operation of the furnace. Taking the actual hourly production data of a steel enterprise's #2 blast furnace in June and July as the basis, the isolated forest algorithm was applied to process the raw data. Through an analysis of the coupling mechanism of blast furnace gas permeability and gas flow, Spearman correlation analysis, and MIC maximum information coefficient analysis of the key parameters for the selection of characteristic variables, a wavelet neural network (WNN) predictive model of the blast furnace gas permeability index was established. The wavelet neural network is based on the introduction of a BP neural network model based on the theory of wavelet transform. It can accurately predict the trend of blast furnace gas permeability, make timely adjustments to the blast furnace production, and ensure the stable and smooth operation of the blast furnace. The innovative points of this paper are as follows:  
1. Based on the actual hourly production data of a blast furnace of an iron and steel enterprise, the isolated forest algorithm is applied to detect, remove, and retain the outliers in the original data and to construct a data-driven set after the deviation normalization process. 2. Characteristic variables were established from the data-driven set by analyzing the coupling mechanism between blast furnace gas permeability and gas flow, as well as performing Spearman correlation analysis and MIC maximum information coefficient analysis. 3. The wavelet neural network algorithm is used to construct an intelligent prediction model of a blast furnace gas permeability index. The wavelet neural network presents obvious advantages when the error is controlled in the range of \(\pm\)0.1, and the prediction accuracy can reach 95.71%. It accurately predicts the change trend in the blast furnace gas permeability index, so as to ensure the stable operation of the furnace. The article is structured as follows: Section 2 describes the isolated forest algorithm as well as departure normalization for data collection and processing. Section 3 describes the coupling mechanism between blast furnace permeability and gas flow. Here, Spearman correlation analysis and MIC maximum information coefficient (MIC) analysis are performed to screen the characteristic variables. Section 4 describes the establishment of the wavelet neural network (WNN) model. Section 5 presents the model comparison evaluation metrics and experimental results. Section 6 summarizes the work of this paper and provides an outlook for the future.