Aiming at the problem of low classification accuracy and slow processing speed in deep classification, a candidate category searching algorithm for text classification was proposed. Firstly, the search, classification of two-stage processing ideas were introduced, and the weighting of the category hierarchy was analyzed and feature was updated dynamically by combining with the structure characteristics of the category hierarchy tree and the related link between categories as well as other implicit domain knowledge. Meanwhile feature set with more classification judgment was built for each node of the category hierarchy tree. In addition, depth first search algorithm was used to reduce the search range and the pruning strategy with setting threshold was applied to search the best candidate category for classified text. Finally, the classical K Nearest Neighbor ( KNN) classification algorithm and Support Vector Machine (SVM) classification algorithm were applied to classification test and contrast analysis on the basis of candidate classes. The experimental results show that the overall classification performance of the proposed algorithm is superior to the traditional classification algorithm, and the average F ₁ value is about 6% higher than the heuristic search algorithm based on greedy strategy. The algorithm improves the classification accuracy of deep text classification significantly.