An Extended Belief Network Recommendation model based on User Dynamic Interaction Behavior （EBNR_UDIB） was proposed to solve the problem of failing to consider accuracy and diversity simultaneously in current recommendation methods due to the unitary way to combine evidence. Firstly， a three-layer basic Belief Network Recommendation （BNR） model was constructed to provide an effective and flexible framework for the introduction of evidence. Secondly， by analyzing direct and coupled interaction relationships among users， the interaction strength was calculated， and this strength was further adjusted by a dynamic time decay factor. Finally， taking the interest of user weighted by this strength as new evidence， EBNR_UDIB was obtained by using two combination ways of evidence： conjunction and disjunction. Experimental results show that compared with Content-Based Recommendation Model （CBRM） and Social relationship-Based Recommendation Model （SBRM）， the proposed model has the accuracy， recall， and F1-measure increased by at least 7， 4， and 5 percentage points respectively under conjunction combination way， and increased by least 2， 8， and 6 percentage points respectively under disjunction combination way； on the diversity and novelty metrics， the proposed model under disjunction combination way is improved by least 15 and 6 percentage points respectively compared to the above two models， and the proposed model under conjunction combination way outperforms the comparison models at the same time.