<p>This study addresses the issue of predicting the operational status of dynamic equipment in crude oil pipelines by introducing a deep learning algorithm. First, the necessity of predicting the operational status of dynamic equipment in crude oil pipelines is analyzed. From the perspectives of data collection, model construction, predictive analysis, and fault diagnosis, a predictive model based on deep learning algorithms is established for the operational status of dynamic equipment in crude oil pipelines. Finally, a case study is conducted using an oil pump along the pipeline as an example to demonstrate the feasibility of deep learning algorithms in predicting the operational status of dynamic equipment in crude oil pipelines. The research shows that dynamic equipment along crude oil pipelines, which operates continuously over long periods, has a relatively high probability of operational failure. The application reveals that using support vector machines and dynamic degradation index calculation methods provides a prediction value and degradation index at a given moment. By using the dynamic degradation of the oil pump as a monitoring indicator, the deep learning algorithm can issue an early warning an average of 15.8 minutes ahead, compared to traditional threshold monitoring methods. Furthermore, it enables fault cause analysis through a knowledge base, yielding significant application effectiveness. The study concludes that deep learning algorithms can provide timely predictions for the operational status of dynamic equipment in crude oil pipelines. To ensure the safe operation of dynamic equipment along crude oil pipelines in the future, this method can be further promoted and applied.</p>