The accurate evaluation of hydraulic fracturing performance is essential for the iterative optimization of unconventional reservoir development. In this aspect, fracturing pressure diagnostics has been recognized as a non-invasive technique that significantly reduces operational time and cost. However, pressure-based diagnostics lack a unified workflow for the evaluation of fracture complexity and area and cannot provide sufficient guidance for design optimization. Thus, this paper proposes an integrated diagnostic framework, constructed by pressure interpretation and data mining, from which the hydraulic fracture complexity and fracture area can be quantified. The normalized fracture complexity index is defined by propagation events and energy intensity extracted from wavelet-transformed pressure signals, and the fracture area is evaluated from pressure falloff analysis. Data mining is then used to optimize the fracturing parameters based on these two indices. The results show that the proposed framework effectively characterizes the stimulated fracture area and complexity and reveals their relationships with fracturing parameters and geological factors on the basis of multi-stage data from three horizontal coalbed methane wells. The stimulated fracture area is primarily determined by the fracturing fluid volume and pumping rate, while the fracture complexity is strongly regulated by the pumping rate and compressive strength of the rock. A negative correlation was detected between the fracture complexity and the main fracture area. To balance the main area and complexity of fractures, it is necessary to optimize the key fracturing parameters. This study provides a low-cost tool that can diagnose hydraulic fracturing performance and effectively optimize unconventional completion.