Pointer analysis is a core and fundamental technology for software compiler optimization and bug detection. Existing classic pointer analysis frameworks such as Doop will transform the programs to be analyzed and analysis algorithms into Datalog evaluation problems like too large program size and solve them. As a result, the analysis time overhead of a single solution can be high, and the program analysis overhead can hardly be afforded especially in situations where programs are frequently changed and released. In recent years, as a technology that effectively reemploys existing analysis results and improves analysis efficiency under frequent code changes, incremental analysis has caught increasing attention. However, since current incremental pointer analysis techniques are often designed for specific algorithms, the supported pointer analysis options are limited and their usability is significantly restricted. To this end, this study designs and implements Differential Doop (DDoop), an incremental pointer analysis framework based on Differential Datalog evaluation. DDoop implements incremental input fact generation and automatic rewriting for incremental analysis rules, expressing incremental analysis problems of multi-version programs as Differential Datalog evaluation problems. Finally, a mature Differential Datalog solution engine like DDlog can be fully utilized to achieve end-to-end incremental pointer analysis, maximizing compatibility and reuse of existing pointer analysis implementations in Doop and providing transparent support for incrementalization. Additionally, experimental evaluation of DDoop is conducted on widely adopted real-world programs. The results show that compared to the non-incremental Doop framework, DDoop has a significant performance advantage while highly compatible with a variety of pointer analysis rules existing in Doop.