Because of the diverse compositions of asphalt pavement damage, multiple cases of damage that are inspected using the same pavement condition index (PCI) may yield different damage combinations. When multiple types of damage coexist but the degree of damage is similar, obtaining targeted maintenance measures with PCI and determining the predominant damage (i.e., most severe road damage with the maximum deduction value) are challenging. Therefore, this study considers PCI analysis and an existing preventive maintenance decision-making method to clarify those sections in which the predominant damage was not well-targeted during inspection and proposes a supplementary approach to make more appropriate conservation decisions. Based on detection and maintenance data of urban roads in Shanghai from over the past five years, a sequential clustering method is used to classify road sections according to their PCI levels. The composition of and difference in pavement damage at different levels are analyzed. Then, for those sections with multiple cases of damage and no significant damage differences, road sections that historically reflect proper preventive maintenance are then selected based on whether effective preventive maintenance can be implemented. Finally, two back propagation (BP) neural network models for preventive maintenance decisions are established and compared based on the effective maintenance road sections. The main differences between the two models are the compositions of pavement damage. The results showed that when the PCI levels were high (84.4~93.0 points), the degrees of damage were very similar and the predominant damage was not represented. Of the two BP neural network models, Model 2,which considered multiple damage components, showed a higher decision accuracy. Specifically, its decision accuracy with the test set reached 86.20%. This was significantly better than that of Model 1 (58.50%), which considered only the predominant damage. Combining the BP neural network and traditional decision tree method can help to optimize decision-making processes related to asphalt pavement and improve the selection of maintenance measures.