Merging probabilistic knowledge bases (PKBs) is an essential task in knowledge representation and reasoning, as it enables the merging of information from multiple sources. Dempster-Shafer (D-S) theory provides a powerful framework for knowledge merging, enabling reasoning and decision-making with incomplete or conflicting information. However, how to employ fusion rules derived from Dempster's rule for merging PKBs is still an open issue. This paper examines the logical relationship between classical probability theory, which represents PKBs through probabilistic constraints, and D-S theory, which represents them using basic probability assignment functions (BPAs). Several fusion rules are investigated, including the Dempster rule (DSR), generalized combination rules (GCR, mGCR). In addition, basic belief functions (BBFs) are studied as a means to refine BPAs and enhance merging quality. Based on these rules and BBFs, methods for merging PKBs are developed, incorporating both single-rule and multi-rule strategies, and algorithm implementing these methods are also proposed. Finally, simulation results, along with discussion, are provided to validate the effectiveness of the proposed methods