This paper proposes a text detection method for multi-oriented scene text detection based on feature reinforcement and adaptive text attention. Firstly, the method constructs different feature reinforcement modules for different levels of features. The high-level feature reinforcement module (HFRM) acquires multi-scale semantic information through the deformed Inception structure to enhance the expression ability of features; the low-level feature reinforcement module (LFRM) captures richer semantic information by constructing a residual-like connection structure to alleviate the semantic gap problem between different levels of features. Besides, the method also designs an adaptive text attention module (ATAM), which can adaptively adjust the attention weights according to the different input features, thus enhancing the fitting ability of the network. We carry out a series of experiments on ICDAR2015, TD500, and ICDAR2017-MLT to confirm the effectiveness of the proposed method.