## 3 Model Building and Algorithm Design
### Prediction Algorithm Design
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Danger**} & **No** & **Danger and** & **Dangerous but Not** & **No Danger and Not** & **No Danger but** \\  & & **Danger** & **Predicted (TP)** & **Predicted (FN)** & **Predicted (TN)** & **Predicted (FP)** \\ \hline \multirow{4}{*}{Times} & Week 1 & 5 & 15 & 5 & 0 & 14 & 1 \\  & Week 2 & 0 & 20 & 0 & 0 & 19 & 1 \\ \cline{1-1}  & Week 3 & 4 & 16 & 3 & 1 & 16 & 0 \\ \cline{1-1}  & Week 4 & 6 & 14 & 5 & 1 & 14 & 0 \\ \cline{1-1}  & Week 5 & 5 & 15 & 5 & 0 & 14 & 1 \\ \cline{1-1}  & Week 6 & 1 & 19 & 1 & 0 & 18 & 1 \\ \hline \multicolumn{2}{c}{Total} & 21 & 99 & 19 & 2 & 95 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of prediction results. the results are up to 17.5%, 54.74%, 66.64%, and 50%, and the prediction time is the lowest. The calculation reliability of the Adagrad algorithm is proven. 4. The results obtained with the mine pressure hazard prediction function in the final example are similar to those produced in practice. Of the 21 pieces of dangerous data, 19 were accurately predicted, with an accuracy of 90.4%. ## 6 Conclusions

The research results of this paper confirm the feasibility of applying the proposed new mine pressure hazard prediction model based on logistic regression into actual mine pressure hazard prediction. This prediction model realizes a more efficient and accurate prediction of mine pressure hazard and can effectively provide theoretical and tool support for mine managers to predict mine pressure hazard and make appropriate decisions in time. However, there are different grades and categories of mine pressure hazards, and the mine conditions of coal mines and metal mines are different. Therefore, in the future, the multi-classification prediction of mine pressure hazard and the characteristics of metal ore will be thoroughly studied to further realize the intelligent decision making of the prediction function. After the system predicts the level and type of mine pressure danger, the induced causes are intelligently analyzed and corresponding solutions are formulated to guide production. Methodological research and writing--original draft preparation, B.Z. ; data curation and supervision, Y.S. ; resources and funding acquisition, J.H. ; investigation, G.F. All authors have read and agreed to the published version of the manuscript. This research was funded by Jian Hao, grant numbers 52174121 and No. 2021 KJ060, and the APC was funded by the National Natural Science Foundation of China and Project of Shan-dong Province Higher Educational \"Youth Innovation Science and Technology Plan\" Team. Not applicable. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The authors declare no conflict of interest. ## References

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