This paper describes our system used in the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition, achieving 3rd for track 1 on the leaderboard. We propose Dictionary-fused BERT, a flexible approach for entity dictionaries integration. The main ideas of our systems are:1) integrating external knowledge (an entity dictionary) into pre-trained models to obtain contextualized word and entity representations 2) designing a robust loss function leveraging a logit matrix 3) adding an auxiliary task, which is an on-top binary classification to decide whether the token is a mention word or not, makes the main task easier to learn. It is worth noting that our system achieves an F1 of 0.914 in the post-evaluation stage by updating the entity dictionary to the one of (CITATION), which is higher than the score of 1st on the leaderboard of the evaluation stage.