Active learning of timed automata is an important research topic. Active learning of multi-clock timed automata is one of the important research directions. However, the existing learning algorithms for multi-clock timed automata have relatively slow learning speed. This study proposes an improved active learning algorithm based on a timed observation tree. A data structure called the timed observation tree is defined to store the information obtained during the learning process. Based on the special structure of the timed observation tree, binary search techniques can be used to analyze counterexamples. By using this counterexample analysis technique, the number of membership queries and reset information queries can be reduced, thereby improving the efficiency of the proposed algorithm. Experimental results demonstrate the effectiveness of the method.