Many crowdsourced workers are using crowdsourced platforms to pursue their second job. They have different testing skills, styles, and preferences. Understanding them is crucial for making collaborative decisions such as crowdsourcing task allocation. The existing crowdsourcing platforms need to provide more information on crowdsourcing workers, and we need to spend a lot of effort searching for this information on crowdsourcing platforms. Unlike the basic information about workers displayed on crowdsourcing platforms, we suggest describing workers as a quick way to understand them. We discussed how to establish concise and informative worker portraits. We propose a multidimensional model for the portrait of Android’s contracted authors to specify the attributes of various aspects of software testing. Then, we proposed a method that utilizes text analysis, network data analysis to analyze various data sources related to personnel on crowdsourced testing platforms to construct portraits. The constructed portrait can be vividly displayed on the internet, helping people quickly understand crowdsourced workers and make better decisions when collaborating on developing testing software. The results indicate the potential for suggested improvements and correct task allocation when using our portrait. Worker portraits are an effective form of describing the characteristics of workers. It can help people quickly understand workers and can be applied in various fields.