To tackle the challenge of multi-sensor information fusion in the absence of prior knowledge, this paper introduces a multi-sensor information evaluation fusion algorithm that incorporates information entropy and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) from the perspective of evaluating the quality of information obtained by sensors in order to obtain results closer to the true value. Firstly, information entropy is used to evaluate the consistency of information obtained by all the sensors at each sampling moment and to weight the sampling moments. TOPSIS is used to evaluate the quality of information obtained by all the sensors and to weight each sensor according to quality ranking. Then, according to the weight of sampling moments and the weight of sensors, a temporary fusion result is obtained and then iterated so that it will converge to the true value. Finally, through a series of simulation experiments, it is demonstrated that the proposed information evaluation fusion algorithm significantly outperforms the existing similar algorithms in terms of stability and accuracy.