Machine learning techniques require an enormous amount of high-quality data labeling for more naturally simulating human comprehension. Recently, mobile crowdsensing, as a new paradigm, makes it possible that a large number of instances can be often quickly labeled at low cost. Existing works only focus on the single labeling for supervised learning problems of traditional machine learning, where one instance associates with only label. However, in many real world applications, an instance may have more than one label. To the end, in this paper, we explore an incremental multi-labeling issue by incentivizing crowd users to label instances under the budget constraint, where each instance is composed of multiple labels. Considering both uncertainty and diversity of the number of each instance’s labels, this paper proposes two mechanisms for incremental multi-labeling crowdsensing by introducing both uncertainty and diversity. Through extensive simulations, we validate their theoretical properties and evaluate the performance.