In order to deal with the highly computational complexity of static attribute reduction when the data increasing dynamically in incomplete hybrid decision system, an incremental attribute reduction method was proposed for incomplete hybrid data with variable precision. The important degrees of attributes were measured by conditional entropy in the variable precision model. Then the incremental updating of conditional entropy and the updating mechanism of attribute reduction were analyzed and designed in detail when the data is dynamically increased. An incremental attribute reduction method was constructed by heuristic greedy strategy which can achieve the dynamical updating of attribute reduction of incomplete numeric and symbolic hybrid data. Through the experimental comparison and analysis of five real hybrid datasets in UCI, in terms of the reduction effects, when the incremental size of the Echocardiogram, Hepatitis, Autos, Credit and Dermatology increased to 90%+10%, the original number of attributes is reduced from 12, 19, 25, 17, 34 to 6, 7, 10, 11, 13, which is accounted for 50.0%, 36.8%, 40.0%, 64.7%, 38.2% of the original attribute set; in terms of the execution time, the average time consumed by the incremental algorithm in the five datasets is 2.99, 3.13, 9.70, 274.19, 50.87 seconds, and the average time consumed by the static algorithm is 284.92, 302.76, 1062.23, 3510.79, 667.85 seconds. The time-consuming of the incremental algorithm is related to the distribution of the instance size, the number of attributes, and the attribute value type of the data set. The experimental results show that the incremental attribute reduction algorithm is significantly superior to the static algorithm in time-consuming, and can effectively eliminate redundant attributes.