The invention discloses a method for predicting fractal traffic flow combining weekly similarity characteristic. The invention comprises the following steps: 1) traffic flow data of different working days takes one week as a period, the traffic flow data are grouped to form traffic flow sequences with different directions at the same intersection in a scheduled period of time; 2) the scheduled time before the current time is extracted to the traffic flow sequences {Ni} of the current time, initialized n is equal to one, and {Si} is obtained through performing n-order cumulative calculation, {Sni}(i=1, ..., n)=N(A, epsilon) I, the obtained value is N(A, epsilon) i+1; 3) according to the traffic flow sequences in the same period of time a week ago, the traffic flow sequences in the same period of time two weeks ago, the traffic flow sequences in the same period of time three weeks ago, ... the traffic flow sequences in the same period of time m weeks ago, the calculations of the step 2) are respectively performed to obtain each predicted data, and the predicted data undergoes error correction to obtain the predicted result data. The invention provides a method for predicting fractal traffic flow combining weekly similarity characteristic with good real-time and high prediction precision.