Industrial control protocols (ICPs), especially proprietary protocols, lack public specifications due to security and commercial considerations, posing profound challenges for security audits and vulnerability detection. Although the function code fields in ICPs define critical operational semantics (e.g., PLC start/stop), existing protocol reverse engineering (PRE) methods struggle to accurately infer the functional implications of their value dependencies. To address this critical gap, we propose FuncSeinfer, an operation-traffic correlation-driven method for inferring functional semantics in proprietary ICPs by associating graphical user interface (GUI) operations of programming software with network traffic. FuncSeinfer first employs an entropy-based dynamic field extraction algorithm to generate candidate fields. Subsequently, it applies heuristic rules and clustering scoring rules to accurately infer function code fields. Finally, it identifies semantics through variance analysis of traffic marked with operational labels. We rigorously evaluated FuncSeinfer on 7 widely used ICPs (S7comm, UMAS-A, UMAS-B, Modbus, PCCC, Melsoft, and Fins) in 9 real-world PLCs from 5 leading manufacturers, achieving 100% accuracy in function code field inference, outperforming state-of-the-art PRE tools such as Netzob, FieldHunter, Netplier, and FSIBP. Furthermore, FuncSeinfer can accurately identify the semantic meaning of 78 function codes, which is an average improvement of 20.2% compared to Wireshark. This study fills the gap in functional semantics within ICP reverse engineering, enabling in-depth security analysis without requiring protocol specifications.