The invention discloses an efficient parallel loading method capable of keeping titan real-time data consistency, and belongs to the field of big data processing. The method comprises the following steps that: firstly, titan is divided into seven modules which work in parallel, and a cleaning rule management module updates a filtering rule in real time; a data receiving module receives and puts pieceOfData into a queue1; a data cleaning module filters qualified data and puts the qualified data into queue2; an ID (Identity) conversion module interacts with a high-speed indexing module to judge whether a corresponding relationship between two points in the current pieceOfData and a titan ID (Identity) is in the presence in a graph database or not; if the corresponding relationship between two points in the current pieceOfData and the titan ID is in the presence in the graph database, an ID attribute in the titan and an ID value replacement point are stored in pieceOfDataT and are put in queue4; otherwise, an unloaded point is put into HashSet, and the corresponding pieceOfDataT is put into queue3; residual data loading modules load the pieceOfDataT into the titan by multiple threads in parallel; and a point loading module is in charge of adding points in the HashSet into the titan, and a corresponding relationship between the points and the titan ID is added into the high-speed indexing module. By use of the method, each module independently or interactively finishes parts of functions so as to realize a purpose that loading efficiency is integrally improved.