For few-shot learning, minimizing the empirical risk cannot reach the optimal hypothesis from image to its label due to the effect of overfitting. Therefore, most of the existing work leverages a set of base classes with sufficient labeled samples to pre-train a general encoder for feature representation, which is then applied for all few-shot classification tasks without considering the uniqueness of the target task. We suppose that different base classes help solve a target task in varying degrees, and some classes even introduce a negative effect. To this end, we propose a Target-guided Base Class Reweighting (TBR) approach, which uses a reweighting-in-the-loop optimization algorithm to assign a set of weights for base classes adaptively given a target task. Specifically, TBR learns the parameter of the encoder via minimizing weighted empirical risk on base class data, then optimizes the weights according to the the encoder's performance on support set of the target task. Such an alternating optimization procedure brings reweighting into the loop which makes the encoder more sensitive to the novel classes of the target task. Extensive experiments demonstrate that the proposed method can improve the performance of model-based approaches on two few-shot classification benchmarks.