This paper proposes a novel transition-based algorithm for character-level Chinese dependency parsing that straightforwardly models the dependen-cy tree in a top-down manner.Based on the stack-pointer parser,we joint Chi-nese word segmentation,part-of-speech tagging,and dependency parsing in a new way.We recursively build the character-based dependency tree from root to leaf in a depth-first fashion,by searching for candidate dependents through the sentence and predicting relation type at each step.We introduce intra-word dependencies into the relation types for word segmentation,and the inter-word dependencies with POS tags for part-of-speech tagging.Since the top-down model provides a global view of an input sentences,the information of the whole sentence and all previously generated arcs are available for action deci-sions,and all characters of the sentence are considered as candidate dependen-cies.Experimental results on the Penn Chinese Treebank(CTB)show that the proposed model outperformed existing neural joint parsers by 0.81%on de-pendency parsing,and achieved the F1-scores of 95.97%,91.72%,80.25%for Chinese word segmentation,part-of-speech tagging,and dependency parsing.