With the popularity of mobile Internet and smart mobile devices in recent years, the app market mode has become one of the main modes of software release. In this mode, app developers have to update their apps rapidly to keep competitive. In comparison with traditional software, the connection between end users and developers of mobile apps is closer with quicker release of software and feedback of users. Understanding and improving user acceptance of mobile apps inevitably becomes one of the main goals for developers to improve their apps. Meanwhile, there is a wealth of data covering different stages of the software cycle of mobile apps in the app-market-centered ecosystem. From the view of software analytics, with techniques such as machine learning and data mining, valuable information could be extracted from data including operation logs, user behavior sequence, etc. to help developers make decisions. This article first demonstrates the necessity and feasibility of building a comprehensive model of user acceptance indicators for mobile apps from a data-driven perspective, and provides basic indicators from three dimensions of user evaluation, operation, and usage. Furthermore, with large-scale datasets, specific indicators are given in three user acceptance prediction tasks, and features from different stages of the software cycle of mobile apps are extracted. With collaborative filtering, regression models, and probability models, the predictability of user acceptance indicators is verified, and the insight of the prediction results in the mobile app development process is provided.