The diverge area is recognized as a common traffic scenario in urban roads. It is characterized by complex traffic characteristics and frequent lane changes, which are known to easily lead to traffic bottlenecks and congestion. Challenges were posed to the improvement of its traffic capacity. To quantitatively analyze the influencing factors of traffic capacity in diverge areas under the intelligent connected context, a comprehensive screening and quantification method was proposed. This method was based on P-value testing and the entropy weight method. Significant variables were firstly screened from potential influencing factors through P-value testing. Weights were then assigned to each factor using the entropy weight method. Key influencing factors were accurately identified. The diverge area of an arterial road was taken as an example. The connectivity penetration rate and diverge ratio were identified as the key influencing factors from both road and traffic dimensions through P-value testing and the entropy weight method. The impacts of these factors on traffic capacity were analyzed based on the simulation of urban mobility(SUMO) simulation platform. The results indicate that the connectivity penetration rate is found to be significantly positively correlated with traffic capacity. The impact of the diverge ratio on traffic capacity is observed to exhibit nonlinear characteristics, with a positive correlation observed when the diverge ratio is below 0.5 and a negative correlation observed when it exceeds 0.5. The influence of connectivity penetration rate on traffic capacity is determined to be significantly higher than that of the diverge ratio. Furthermore, by exploring the implementation effects of dedicated lanes for connected and automated vehicles(CAV) under different penetration rates, it provides theoretical basis and practical guidance for the promotion of intelligent connected technologies and the optimization of traffic capacity in diversion areas.