The scheduling of railway networks is becoming more vulnerable to operational disturbances due to increasing traffic demand and limited infrastructure expansion. This leads to severe outcomes including the breaking of the connection between adjacent trains, deterioration of the service levels for customers, and even the collapse of the entire railway operation. Therefore, it is important to design a railway system that is highly-resistant against operational disturbances. For both robust timetabling and dynamic dispatching based approaches, identifying critical railway block sections is always the prerequisite. This study conducts an operational risk analysis by developing an “operational risk index” (RI) based on statistical methods and railway simulation tools. In the proposed algorithm, random disturbances are firstly artificially imposed on a target block section and the influences (RI) on the entire railway network are then calculated to evaluate the operational risk of this target block section. A case study is conducted on a reference railway network. Results indicate that the algorithm proposed within is capable of thoroughly evaluating the operational risk level of block sections and is also compatible with different distribution types of random disturbances, applicable to different traffic volumes (condensed timetables), and stable among basic timetables with slight adjustments of freight transport schedules.