Natural-scene text-detection algorithms based on deep learning have made significant progress； however， they only apply to texts with dense and irregular layouts. Owing to its small spacing and dense distribution， it is difficult to extract features from texts and the detection remains incomplete. Meanwhile， the existing text detection methods often use the direct splicing of different dimensional features， leading to insufficient multi-scale feature fusion and the loss of semantic information. To solve these problems， a dense irregular text detection method is proposed based on multi-dimensional convolution fusion. The network follows the FPN structure and utilizes a text enhancement module （TEM）. By using additional global text mapping， the network pays special attention to the text information. A channel fusion strategy （CFS） is proposed， which uses the bottom-up method to establish the high-low dimension feature information chain to generate the feature map with richer semantics and reduce the information loss. In the prediction stage， text prediction results are generated through the gradual expansion of the text kernel. Experimental results on DAST1500， ICDAR2015， and CTW1500 datasets yield F values of 81.8%， 83.8%， and 79.0% respectively. The proposed algorithm not only has better performance in dense and irregular text detection but also shows a certain level of competitiveness in the case of general natural scene texts （multi-directional， curvilinear text）.