  The shield machine (SM) is a complex mechanical device used for tunneling.
However, the monitoring and deciding were mainly done by artificial experience
during traditional construction, which brought some limitations, such as hidden
mechanical failures, human operator error, and sensor anomalies. To deal with
these challenges, many scholars have studied SM intelligent methods. Most of
these methods only take SM into account but do not consider the SM operating
environment. So, this paper discussed the relationship among SM, geological
information, and control terminals. Then, according to the relationship, models
were established for the control terminal, including SM rate prediction and SM
anomaly detection. The experimental results show that compared with baseline
models, the proposed models in this paper perform better. In the proposed
model, the R2 and MSE of rate prediction can reach 92.2\%, and 0.0064
respectively. The abnormal detection rate of anomaly detection is up to 98.2\%.
