Various safety surrogate indicators have been used to identify conflicts within intersections, however most of them are limited to assess the conflict risk without the consideration of conflict consequences. At the mixed intersection, the conflict consequences are critical measurements for risk assessment due to the heterogeneity of traffic participants. Therefore, we proposed a Traffic Conflict Criticality Index (TCCI) to identify conflict risk considering heterogeneity of traffic participants. The coulomb force theory is applied to the field of traffic safety, viewing different traffic participants as particles with varying charges and identifying conflicts by analyzing the forces between them. In this model, energy and a heterogeneity index are used to identify and quantify the conflict risk between different traffic participants. The velocities and accelerations of different traffic participants are used as input features and thresholds for different conflict levels are determined through the k-means clustering algorithm. The results of a case study from InD dataset, demonstrate that the proposed TCCI model has high accuracy in identifying traffic conflicts, avoiding misjudgments, especially when considering the heterogeneity of traffic participants, accurately identifying 495 serious conflicts, comparing to the traditional TTC model. The model proposed in this paper has a high conflict prediction capability, effectively identifying and differentiating the conflict risks of different traffic participants at intersections. The application of this model provides an alternative tool to enhance traffic safety management at urban intersections, with particular emphasis on the safety of vulnerable groups.