In the era of knowledge economy, influential innovative output can bring immeasurable economic benefits, while lack of innovation may lead to stagnation and regression of enterprise. So far scholars have not reached a consensus on the relationships between explorative search and the impact of innovative output, in which inter-organizational attributes, taken as the most basic factor affecting the effectiveness of explorative search still need deconstruction. As a knowledge-intensive high-tech industry with high R&D intensity and frequent innovative search activities, the semiconductor industry is regarded as a case in point. To answer the research question “how explorative search affects the impact of innovative output”, JAVA and R language, text mining, database technology, social network analysis technology are used to construct an internal network model and measure the core variables on the basis of large-scale USPTO patent data (totaling approximately 187,000 patents, approximately 1.329 million backward citations and 2.082 million forward citations). We identify 63 listed companies in the global semiconductor industry between 1991 and 2000 as our research objects, and through negative binomial regression analysis of the panel data with 630 observed values, this study reaches the following conclusions: First, explorative search, as a key factor of the impact of innovative output, exerts a positive effect on it, which means that searching for unfamiliar knowledge can add new knowledge elements to organizational knowledge base and increase heterogeneity. To be specific, doing this can increase the possibility of creative knowledge combination, challenge the current cognitive structure and causal belief, and as a result increase the emergency likelihood of high-impact innovative output. Second, inter-organizational collaboration network is the important internal context factor which influences the relationship between explorative search and the impact of innovative output. On the one hand, the density of inter-organizational collaboration networks has a positive moderation effect on the relationship between explorative search and the impact of innovative output. When the density of inter-organizational collaboration network is high, tacit knowledge is more favorable to transmission, and the likelihood of using new knowledge by others is increasing as well, which lead to improving the possibility of high-impact innovative output. On the other hand, the separation degree of inter-organizational collaboration networks negatively moderates the main effect for the reason that, as the number of unconnected discrete small group increases, it enlarges the communication gap between each other, which is harmful to the knowledge transfer and thus decreases the likelihood of new discovery. Compared with previous studies, this study contributes to the literature in the following aspects: First, this study performs a meticulous deconstruction of the inter-organizational context from the perspective of network structure. Deconstructing the internal network configuration provides a helpful research direction for studying the effect of explorative search on innovation. Second, this study supplements the current research on innovative search from the perspective of the technological cognitive boundary. Existing researches mainly define the crossing of technological cognitive boundary as searching for knowledge outside the industry, but this approach is doubtful from the perspective of construct measurement. This study defines the technological cognitive boundary from its essence, i.e. on the ground that the knowledge is familiar to the organization. So it overcomes the shortage of direct empirical tests on the relationship between explorative search (searching the knowledge never used before) and the impact of innovative output.