Digital finance (DF) has provided important financial support for the transformation and upgrading of China’s manufacturing industry. Innovation is the engine of industrial upgrading. To solve the dilemma of developing the manufacturing industry, it is necessary to enhance independent innovation capabilities. On this basis, this article studies the impact of DF on manufacturing technology innovation (MTI). It uses the data of listed manufacturing firms in the Shenzhen and Shanghai A-share markets from 2011 to 2020 to establish a fixed-effects model and a panel-threshold model for empirical analysis. The results revealed that, first, DF significantly accelerates technological innovation in manufacturing enterprises and has a significant positive impact on technological innovation. Secondly, DF drives manufacturing enterprises’ technological innovation by alleviating financial constraints (FCs). Thirdly, there is a dual-threshold effect based on market competition between DF and MTI based on market competition, and the promotion effect of DF on technology innovation decreases with the increasing degree of market competition. Finally, DF better enhances the technological innovation of non-state-owned manufacturing firms in the respective regions compared to state-owned firms. In terms of factor-intensive types, DF is more able to advance the innovative technologies of labor-intensive and capital-intensive enterprises, while it has no significant positive effect on technology-intensive enterprises. Policy implications are suggested to boost manufacturing technology innovation and aid future studies.

As a modern comprehensive information platform for integrated statistical analysis of express shipments information and for express shipments management decision-making, the express logistics track and trace system needs to use artificial intelligence (AI) technology and image processing technology to automatically extract the text content of express logistics documents. Existing express shipments information identification models usually have problems such as less-than-ideal performance in detecting single characters or small text regions of express logistics documents, high human resource cost for character-level markup, and low speed and accuracy of text recognition. In response, this paper studies the image-processing-based identification method of express logistics information. It presents a recognition process for pre-processing text images of express logistics documents, along with a detailed description of denoising, greyscaling and binarisation methods. While proposing an enhancement strategy for Chinese characters in the section of handwritten Chinese, this paper constructs a model for recognition of express shipping document texts based on bidirectional long-short term memory (LSTM) and attention mechanism. In this way, we fully mined key semantic information of express logistics document texts. The experimental results verify the effectiveness of the constructed model.